Add UPP STL asset indexing workflow

UPP_数据库构建/01_UPP资产索引建表.sql

@@ -0,0 +1,92 @@
+CREATE TABLE IF NOT EXISTS upp_exam_assets (
+  ct_number text NOT NULL PRIMARY KEY,
+  list_present boolean NOT NULL DEFAULT false,
+  stl_present boolean NOT NULL DEFAULT false,
+  patient_name text,
+  patient_sex text,
+  patient_age text,
+  patient_id_masked text,
+  exam_date timestamptz,
+  task_created_at timestamptz,
+  exam_description text,
+  exam_device text,
+  algorithm_model text,
+  upp_status text,
+  list_record_count integer NOT NULL DEFAULT 0,
+  selected_list_record jsonb,
+  list_records jsonb NOT NULL DEFAULT '[]'::jsonb,
+  selected_source_case_dir text,
+  selected_source_stl_dir text,
+  processed_stl_dir text,
+  stl_case_name text,
+  stl_sequence_no integer,
+  stl_file_count integer NOT NULL DEFAULT 0,
+  stl_total_bytes bigint NOT NULL DEFAULT 0,
+  stl_files jsonb NOT NULL DEFAULT '[]'::jsonb,
+  stl_candidates jsonb NOT NULL DEFAULT '[]'::jsonb,
+  updated_at timestamptz NOT NULL DEFAULT now(),
+  CONSTRAINT ck_upp_exam_assets_ct_number_present CHECK (btrim(ct_number) <> ''),
+  CONSTRAINT ck_upp_exam_assets_ct_number_format CHECK (ct_number ~ '^D?CT[0-9]{8,}$')
+);
+
+DO $$
+BEGIN
+  IF to_regclass('public.upp_stl_files') IS NOT NULL
+     AND NOT EXISTS (
+       SELECT 1
+       FROM information_schema.columns
+       WHERE table_schema = 'public'
+         AND table_name = 'upp_stl_files'
+         AND column_name = 'file_names'
+     )
+  THEN
+    CREATE TABLE IF NOT EXISTS upp_stl_files_row_detail_backup AS TABLE upp_stl_files;
+    DROP TABLE upp_stl_files;
+  END IF;
+END $$;
+
+CREATE TABLE IF NOT EXISTS upp_stl_files (
+  ct_number text NOT NULL PRIMARY KEY REFERENCES upp_exam_assets(ct_number) ON DELETE CASCADE,
+  file_count integer NOT NULL DEFAULT 0,
+  total_bytes bigint NOT NULL DEFAULT 0,
+  segment_names jsonb NOT NULL DEFAULT '[]'::jsonb,
+  segment_families jsonb NOT NULL DEFAULT '[]'::jsonb,
+  segment_categories jsonb NOT NULL DEFAULT '[]'::jsonb,
+  file_names jsonb NOT NULL DEFAULT '[]'::jsonb,
+  source_file_paths jsonb NOT NULL DEFAULT '[]'::jsonb,
+  processed_file_paths jsonb NOT NULL DEFAULT '[]'::jsonb,
+  files jsonb NOT NULL DEFAULT '[]'::jsonb,
+  updated_at timestamptz NOT NULL DEFAULT now()
+);
+
+ALTER TABLE upp_exam_assets ALTER COLUMN ct_number SET NOT NULL;
+ALTER TABLE upp_stl_files ALTER COLUMN ct_number SET NOT NULL;
+ALTER TABLE upp_stl_files ADD COLUMN IF NOT EXISTS segment_families jsonb NOT NULL DEFAULT '[]'::jsonb;
+ALTER TABLE upp_stl_files ADD COLUMN IF NOT EXISTS segment_categories jsonb NOT NULL DEFAULT '[]'::jsonb;
+
+CREATE INDEX IF NOT EXISTS idx_upp_exam_assets_list_present ON upp_exam_assets(list_present);
+CREATE INDEX IF NOT EXISTS idx_upp_exam_assets_stl_present ON upp_exam_assets(stl_present);
+CREATE INDEX IF NOT EXISTS idx_upp_exam_assets_patient_name ON upp_exam_assets(patient_name);
+CREATE INDEX IF NOT EXISTS idx_upp_exam_assets_exam_date ON upp_exam_assets(exam_date);
+CREATE INDEX IF NOT EXISTS idx_upp_exam_assets_list_records_gin ON upp_exam_assets USING gin (list_records);
+CREATE INDEX IF NOT EXISTS idx_upp_exam_assets_stl_files_gin ON upp_exam_assets USING gin (stl_files);
+CREATE INDEX IF NOT EXISTS idx_upp_stl_files_segment_names_gin ON upp_stl_files USING gin (segment_names);
+CREATE INDEX IF NOT EXISTS idx_upp_stl_files_segment_families_gin ON upp_stl_files USING gin (segment_families);
+CREATE INDEX IF NOT EXISTS idx_upp_stl_files_segment_categories_gin ON upp_stl_files USING gin (segment_categories);
+CREATE INDEX IF NOT EXISTS idx_upp_stl_files_file_names_gin ON upp_stl_files USING gin (file_names);
+CREATE INDEX IF NOT EXISTS idx_upp_stl_files_files_gin ON upp_stl_files USING gin (files);
+
+COMMENT ON TABLE upp_exam_assets IS 'UPP列表、STL重建结果、未来CT数据的CT号唯一资产索引';
+COMMENT ON COLUMN upp_exam_assets.ct_number IS 'CT检查号，唯一索引键';
+COMMENT ON COLUMN upp_exam_assets.list_records IS '来自UPP列表OCR的同CT号全部记录';
+COMMENT ON COLUMN upp_exam_assets.selected_list_record IS '同CT号列表记录中按任务创建时间/检查时间选择的新记录';
+COMMENT ON COLUMN upp_exam_assets.stl_candidates IS '同CT号全部候选STL目录，保留去重选择依据';
+COMMENT ON COLUMN upp_exam_assets.stl_files IS '最终选中STL目录的文件清单';
+COMMENT ON COLUMN upp_exam_assets.processed_stl_dir IS '规范化后的STL目录，目录名为CT号';
+COMMENT ON TABLE upp_stl_files IS 'UPP最终选中STL文件聚合表，每个CT号唯一一行';
+COMMENT ON COLUMN upp_stl_files.ct_number IS 'CT检查号，主键并关联upp_exam_assets.ct_number';
+COMMENT ON COLUMN upp_stl_files.segment_names IS '最终选中STL文件的分割名称JSON数组';
+COMMENT ON COLUMN upp_stl_files.segment_families IS '与segment_names同顺序的训练family JSON数组';
+COMMENT ON COLUMN upp_stl_files.segment_categories IS '与segment_names同顺序的粗分类JSON数组';
+COMMENT ON COLUMN upp_stl_files.file_names IS '最终选中STL文件名JSON数组';
+COMMENT ON COLUMN upp_stl_files.files IS '最终选中STL文件完整明细JSON数组';

UPP_数据库构建/02_同步UPP_STL资产.py

@@ -0,0 +1,672 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""整理UPP STL目录，并把CT号唯一资产索引同步到PostgreSQL。"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import os
+import re
+import shutil
+import subprocess
+import tempfile
+import unicodedata
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+
+BASE_DIR = Path(__file__).resolve().parents[1]
+DEFAULT_STL_ROOT = BASE_DIR / "UPP_STL处理" / "待处理STL数据"
+DEFAULT_PROCESSED_ROOT = BASE_DIR / "UPP_STL处理" / "已处理STL数据"
+DEFAULT_LIST_JSON = BASE_DIR / "UPP列表处理" / "数据处理结果区" / "全量分片结果" / "合并_图片表格_结构化.json"
+DEFAULT_SCHEMA = BASE_DIR / "UPP_数据库构建" / "01_UPP资产索引建表.sql"
+DEFAULT_REPORT = BASE_DIR / "UPP_数据库构建" / "UPP_STL资产同步报告.json"
+CT_PATTERN = re.compile(r"(D?CT\d{8,})", re.IGNORECASE)
+VALID_CT_PATTERN = re.compile(r"^D?CT\d{8,}$")
+
+
+ASSET_FIELDS = [
+    "ct_number",
+    "list_present",
+    "stl_present",
+    "patient_name",
+    "patient_sex",
+    "patient_age",
+    "patient_id_masked",
+    "exam_date",
+    "task_created_at",
+    "exam_description",
+    "exam_device",
+    "algorithm_model",
+    "upp_status",
+    "list_record_count",
+    "selected_list_record",
+    "list_records",
+    "selected_source_case_dir",
+    "selected_source_stl_dir",
+    "processed_stl_dir",
+    "stl_case_name",
+    "stl_sequence_no",
+    "stl_file_count",
+    "stl_total_bytes",
+    "stl_files",
+    "stl_candidates",
+]
+
+STL_FIELDS = [
+    "ct_number",
+    "file_count",
+    "total_bytes",
+    "segment_names",
+    "segment_families",
+    "segment_categories",
+    "file_names",
+    "source_file_paths",
+    "processed_file_paths",
+    "files",
+]
+
+MODEL_VALUES = {"肝胆模型", "泌尿模型", "胸外模型"}
+DEVICE_VALUES = {"CT", "MR", "DR", "CR", "DX", "US", "XA", "NM", "PT"}
+
+
+def normalize_text(value: Any) -> str:
+    if value is None:
+        return ""
+    text = unicodedata.normalize("NFKC", str(value)).replace("\u3000", " ")
+    return re.sub(r"\s+", " ", text).strip()
+
+
+def normalize_ct(value: Any) -> str:
+    return re.sub(r"\s+", "", normalize_text(value)).upper()
+
+
+def valid_ct(value: str) -> bool:
+    return bool(VALID_CT_PATTERN.fullmatch(value))
+
+
+def extract_ct(text: str) -> str:
+    match = CT_PATTERN.search(text)
+    if not match:
+        return ""
+    ct_number = normalize_ct(match.group(1))
+    return ct_number if valid_ct(ct_number) else ""
+
+
+def json_dump(value: Any) -> str:
+    return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
+
+
+def path_text(path: Path) -> str:
+    return str(path.expanduser().absolute())
+
+
+def parse_time(value: Any) -> datetime | None:
+    text = normalize_text(value)
+    if not text:
+        return None
+    for fmt in ("%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y-%m-%d", "%Y/%m/%d"):
+        try:
+            return datetime.strptime(text, fmt)
+        except ValueError:
+            pass
+    return None
+
+
+def normalize_status(value: Any) -> str:
+    text = normalize_text(value).replace("\ufe0f", "")
+    compact = re.sub(r"\s+", "", text)
+    if not compact:
+        return ""
+    if "部分重建成功" in compact:
+        return "√ 部分重建成功"
+    if "重建失败" in compact or compact.startswith(("×", "✗")):
+        return "× 重建失败"
+    if "重建成功" in compact:
+        return "√ 重建成功"
+    if "已报告" in compact:
+        return "√ 已报告"
+    return text
+
+
+def status_like(value: Any) -> bool:
+    text = normalize_text(value)
+    return bool(re.search(r"(重建成功|重建失败|部分重建成功|已报告)", text))
+
+
+def classify_segment(segment_name: str) -> tuple[str, str]:
+    if segment_name in {"liver", "liver_left", "liver_right"}:
+        return "肝脏主体", segment_name
+    if re.fullmatch(r"liver_segment_S[1-8]", segment_name):
+        return "肝段", segment_name
+    if segment_name in {"liver_artery", "liver_vein", "portal_vein", "bile_duct"}:
+        return "血管胆管", segment_name
+    if segment_name in {"pancreas", "spleen", "cholecyst"}:
+        return "腹部脏器", segment_name
+    if segment_name in {"skin", "rib", "vertebrae", "sternum", "hipbone", "sacrum"}:
+        return "体表骨骼", segment_name
+    if re.fullmatch(r"liver_tumor_\d+", segment_name):
+        return "肝脏肿瘤", "liver_tumor_*"
+    if re.fullmatch(r"liver_cyst_\d+", segment_name):
+        return "肝囊肿", "liver_cyst_*"
+    if re.fullmatch(r"liver_hemangioma_\d+", segment_name):
+        return "肝血管瘤", "liver_hemangioma_*"
+    if re.fullmatch(r"pancreas_tumor_\d+", segment_name):
+        return "胰腺肿瘤", "pancreas_tumor_*"
+    if re.fullmatch(r"Segment_\d+", segment_name):
+        return "未命名分割", "Segment_*"
+    return "其他", segment_name
+
+
+def clean_record_info(info: dict[str, Any]) -> dict[str, Any]:
+    cleaned = dict(info)
+    exam_description = normalize_text(cleaned.get("检查描述"))
+    exam_device = normalize_text(cleaned.get("检查设备"))
+    algorithm_model = normalize_text(cleaned.get("算法模型"))
+    if exam_description in DEVICE_VALUES and exam_device in MODEL_VALUES and status_like(algorithm_model):
+        cleaned["检查描述"] = ""
+        cleaned["检查设备"] = exam_description
+        cleaned["算法模型"] = exam_device
+        cleaned["状态"] = normalize_status(algorithm_model)
+    elif exam_device in MODEL_VALUES and status_like(algorithm_model) and normalize_text(cleaned.get("状态")) in {"土", "士", "↓"}:
+        cleaned["检查设备"] = "CT"
+        cleaned["算法模型"] = exam_device
+        cleaned["状态"] = normalize_status(algorithm_model)
+    else:
+        cleaned["状态"] = normalize_status(cleaned.get("状态"))
+    return cleaned
+
+
+def clean_record(record: dict[str, Any]) -> dict[str, Any]:
+    cleaned = dict(record)
+    info = cleaned.get("记录信息")
+    if isinstance(info, dict):
+        cleaned["记录信息"] = clean_record_info(info)
+    return cleaned
+
+
+def sql_quote_path(path: Path) -> str:
+    return "'" + str(path.expanduser().absolute()).replace("'", "''") + "'"
+
+
+@dataclass(frozen=True)
+class StlCandidate:
+    ct_number: str
+    source_case_dir: Path
+    source_stl_dir: Path
+    sequence_no: int | None
+    files: tuple[Path, ...]
+
+    @property
+    def file_count(self) -> int:
+        return len(self.files)
+
+    @property
+    def total_bytes(self) -> int:
+        return sum(file.stat().st_size for file in self.files)
+
+    def score(self) -> tuple[int, int, str, str]:
+        return (
+            self.file_count,
+            self.sequence_no if self.sequence_no is not None else -1,
+            self.source_case_dir.name,
+            self.source_stl_dir.name,
+        )
+
+    def summary(self) -> dict[str, Any]:
+        return {
+            "source_case_dir": path_text(self.source_case_dir),
+            "source_stl_dir": path_text(self.source_stl_dir),
+            "case_name": self.source_case_dir.name,
+            "sequence_no": self.sequence_no,
+            "file_count": self.file_count,
+            "total_bytes": self.total_bytes,
+        }
+
+
+def sequence_no(case_dir: Path, stl_dir: Path) -> int | None:
+    values: list[int] = []
+    for pattern in (r"-(\d{3,6})_D?CT", r"^(\d{3,6})-STL$"):
+        for text in (case_dir.name, stl_dir.name):
+            values.extend(int(item) for item in re.findall(pattern, text, re.IGNORECASE))
+    return max(values) if values else None
+
+
+def scan_stl_candidates(stl_root: Path) -> tuple[dict[str, list[StlCandidate]], list[str]]:
+    candidates: dict[str, list[StlCandidate]] = {}
+    no_ct_dirs: list[str] = []
+    for directory in sorted(stl_root.rglob("*")):
+        if not directory.is_dir():
+            continue
+        files = tuple(sorted([item for item in directory.iterdir() if item.is_file() and item.suffix.lower() == ".stl"]))
+        if not files:
+            continue
+        ct_number = extract_ct("/".join(directory.relative_to(stl_root).parts))
+        if not ct_number:
+            no_ct_dirs.append(path_text(directory))
+            continue
+        candidate = StlCandidate(
+            ct_number=ct_number,
+            source_case_dir=directory.parent,
+            source_stl_dir=directory,
+            sequence_no=sequence_no(directory.parent, directory),
+            files=files,
+        )
+        candidates.setdefault(ct_number, []).append(candidate)
+    return candidates, no_ct_dirs
+
+
+def choose_candidates(candidates: dict[str, list[StlCandidate]]) -> dict[str, StlCandidate]:
+    return {ct_number: max(items, key=lambda item: item.score()) for ct_number, items in candidates.items()}
+
+
+def clear_directory(directory: Path) -> None:
+    directory.mkdir(parents=True, exist_ok=True)
+    for child in directory.iterdir():
+        if child.is_dir() and not child.is_symlink():
+            shutil.rmtree(child)
+        else:
+            child.unlink()
+
+
+def place_file(source: Path, destination: Path, mode: str) -> str:
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    if mode == "symlink":
+        destination.symlink_to(source.expanduser().absolute())
+        return "symlink"
+    if mode == "copy":
+        shutil.copy2(source, destination)
+        return "copy"
+    try:
+        os.link(source, destination)
+        return "hardlink"
+    except OSError:
+        shutil.copy2(source, destination)
+        return "copy"
+
+
+def materialize_selected_stl(
+    selected: dict[str, StlCandidate],
+    processed_root: Path,
+    link_mode: str,
+    refresh_files: bool,
+) -> tuple[dict[str, list[dict[str, Any]]], dict[str, str]]:
+    processed_files: dict[str, list[dict[str, Any]]] = {}
+    link_methods: dict[str, str] = {}
+    processed_root.mkdir(parents=True, exist_ok=True)
+    for ct_number, candidate in sorted(selected.items()):
+        ct_dir = processed_root / ct_number
+        if refresh_files:
+            clear_directory(ct_dir)
+        else:
+            ct_dir.mkdir(parents=True, exist_ok=True)
+        file_rows: list[dict[str, Any]] = []
+        used_methods: set[str] = set()
+        for source in candidate.files:
+            destination = ct_dir / source.name
+            if destination.exists() or destination.is_symlink():
+                destination.unlink()
+            method = place_file(source, destination, link_mode)
+            used_methods.add(method)
+            category, family = classify_segment(source.stem)
+            file_rows.append(
+                {
+                    "segment_name": source.stem,
+                    "family": family,
+                    "category": category,
+                    "file_name": source.name,
+                    "source_file_path": path_text(source),
+                    "processed_file_path": path_text(destination),
+                    "size_bytes": source.stat().st_size,
+                }
+            )
+        manifest = {
+            "ct_number": ct_number,
+            "selected_source_case_dir": path_text(candidate.source_case_dir),
+            "selected_source_stl_dir": path_text(candidate.source_stl_dir),
+            "processed_stl_dir": path_text(ct_dir),
+            "selection_rule": "file_count_desc_then_sequence_no_desc",
+            "stl_file_count": candidate.file_count,
+            "stl_total_bytes": candidate.total_bytes,
+            "files": file_rows,
+        }
+        (ct_dir / "manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
+        processed_files[ct_number] = file_rows
+        link_methods[ct_number] = "+".join(sorted(used_methods))
+    return processed_files, link_methods
+
+
+def load_list_records(list_json: Path) -> tuple[dict[str, list[dict[str, Any]]], list[dict[str, Any]]]:
+    data = json.loads(list_json.read_text(encoding="utf-8"))
+    records_by_ct: dict[str, list[dict[str, Any]]] = {}
+    invalid: list[dict[str, Any]] = []
+    for index, record in enumerate(data.get("图片表格记录", []), start=1):
+        record = clean_record(record)
+        info = record.get("记录信息") or {}
+        ct_number = normalize_ct(info.get("检查号", ""))
+        if not valid_ct(ct_number):
+            invalid.append({"row_index": index, "ct_number": ct_number, "record": record})
+            continue
+        records_by_ct.setdefault(ct_number, []).append(record)
+    return records_by_ct, invalid
+
+
+def select_list_record(records: list[dict[str, Any]]) -> dict[str, Any]:
+    def key(record: dict[str, Any]) -> tuple[datetime, datetime, int, int]:
+        info = record.get("记录信息") or {}
+        image = record.get("图片信息") or {}
+        task_time = parse_time(info.get("任务创建时间")) or datetime.min
+        exam_time = parse_time(info.get("检查日期")) or datetime.min
+        page_no = -1
+        seq = image.get("图片序号")
+        if isinstance(seq, list):
+            numbers = [item for item in seq if isinstance(item, int)]
+            if numbers:
+                page_no = numbers[0]
+        row_no = int(image.get("图片内行号") or -1)
+        return (task_time, exam_time, page_no, row_no)
+
+    return max(records, key=key)
+
+
+def build_rows(
+    selected: dict[str, StlCandidate],
+    all_candidates: dict[str, list[StlCandidate]],
+    processed_root: Path,
+    processed_files: dict[str, list[dict[str, Any]]],
+    list_records: dict[str, list[dict[str, Any]]],
+) -> tuple[list[dict[str, Any]], list[dict[str, Any]], int]:
+    asset_rows: list[dict[str, Any]] = []
+    stl_rows: list[dict[str, Any]] = []
+    stl_file_count = 0
+    for ct_number in sorted(set(selected) | set(list_records)):
+        candidate = selected.get(ct_number)
+        records = list_records.get(ct_number, [])
+        chosen_record = select_list_record(records) if records else None
+        info = (chosen_record or {}).get("记录信息") or {}
+        files = processed_files.get(ct_number, [])
+        processed_dir = processed_root / ct_number
+        asset_rows.append(
+            {
+                "ct_number": ct_number,
+                "list_present": bool(records),
+                "stl_present": candidate is not None,
+                "patient_name": normalize_text(info.get("姓名")),
+                "patient_sex": normalize_text(info.get("性别")),
+                "patient_age": normalize_text(info.get("年龄")),
+                "patient_id_masked": normalize_text(info.get("患者号")),
+                "exam_date": normalize_text(info.get("检查日期")),
+                "task_created_at": normalize_text(info.get("任务创建时间")),
+                "exam_description": normalize_text(info.get("检查描述")),
+                "exam_device": normalize_text(info.get("检查设备")),
+                "algorithm_model": normalize_text(info.get("算法模型")),
+                "upp_status": normalize_text(info.get("状态")),
+                "list_record_count": len(records),
+                "selected_list_record": json_dump(chosen_record) if chosen_record else "",
+                "list_records": json_dump(records),
+                "selected_source_case_dir": path_text(candidate.source_case_dir) if candidate else "",
+                "selected_source_stl_dir": path_text(candidate.source_stl_dir) if candidate else "",
+                "processed_stl_dir": path_text(processed_dir) if candidate else "",
+                "stl_case_name": candidate.source_case_dir.name if candidate else "",
+                "stl_sequence_no": candidate.sequence_no if candidate and candidate.sequence_no is not None else "",
+                "stl_file_count": candidate.file_count if candidate else 0,
+                "stl_total_bytes": candidate.total_bytes if candidate else 0,
+                "stl_files": json_dump(files),
+                "stl_candidates": json_dump([item.summary() for item in sorted(all_candidates.get(ct_number, []), key=lambda item: item.score(), reverse=True)]),
+            }
+        )
+        if files:
+            stl_file_count += len(files)
+            stl_rows.append(
+                {
+                    "ct_number": ct_number,
+                    "file_count": len(files),
+                    "total_bytes": sum(int(file_info.get("size_bytes") or 0) for file_info in files),
+                    "segment_names": json_dump([file_info["segment_name"] for file_info in files]),
+                    "segment_families": json_dump([file_info["family"] for file_info in files]),
+                    "segment_categories": json_dump([file_info["category"] for file_info in files]),
+                    "file_names": json_dump([file_info["file_name"] for file_info in files]),
+                    "source_file_paths": json_dump([file_info["source_file_path"] for file_info in files]),
+                    "processed_file_paths": json_dump([file_info["processed_file_path"] for file_info in files]),
+                    "files": json_dump(files),
+                }
+            )
+    return asset_rows, stl_rows, stl_file_count
+
+
+def write_csv(rows: list[dict[str, Any]], fields: list[str], suffix: str) -> Path:
+    temp_file = tempfile.NamedTemporaryFile("w", encoding="utf-8", newline="", suffix=suffix, delete=False)
+    with temp_file:
+        writer = csv.DictWriter(temp_file, fieldnames=fields)
+        writer.writeheader()
+        for row in rows:
+            writer.writerow({field: row.get(field, "") for field in fields})
+    return Path(temp_file.name)
+
+
+def run_psql(args: argparse.Namespace, asset_csv: Path, stl_csv: Path) -> None:
+    sql = f"""
+\\set ON_ERROR_STOP on
+\\i {sql_quote_path(Path(args.schema))}
+CREATE TEMP TABLE stg_upp_exam_assets (
+  ct_number text,
+  list_present boolean,
+  stl_present boolean,
+  patient_name text,
+  patient_sex text,
+  patient_age text,
+  patient_id_masked text,
+  exam_date timestamptz,
+  task_created_at timestamptz,
+  exam_description text,
+  exam_device text,
+  algorithm_model text,
+  upp_status text,
+  list_record_count integer,
+  selected_list_record text,
+  list_records text,
+  selected_source_case_dir text,
+  selected_source_stl_dir text,
+  processed_stl_dir text,
+  stl_case_name text,
+  stl_sequence_no integer,
+  stl_file_count integer,
+  stl_total_bytes bigint,
+  stl_files text,
+  stl_candidates text
+);
+CREATE TEMP TABLE stg_upp_stl_files (
+  ct_number text,
+  file_count integer,
+  total_bytes bigint,
+  segment_names text,
+  segment_families text,
+  segment_categories text,
+  file_names text,
+  source_file_paths text,
+  processed_file_paths text,
+  files text
+);
+\\copy stg_upp_exam_assets({",".join(ASSET_FIELDS)}) FROM {sql_quote_path(asset_csv)} WITH (FORMAT csv, HEADER true, NULL '')
+\\copy stg_upp_stl_files({",".join(STL_FIELDS)}) FROM {sql_quote_path(stl_csv)} WITH (FORMAT csv, HEADER true, NULL '')
+INSERT INTO upp_exam_assets (
+  ct_number, list_present, stl_present, patient_name, patient_sex, patient_age, patient_id_masked,
+  exam_date, task_created_at, exam_description, exam_device, algorithm_model, upp_status,
+  list_record_count, selected_list_record, list_records, selected_source_case_dir, selected_source_stl_dir,
+  processed_stl_dir, stl_case_name, stl_sequence_no, stl_file_count, stl_total_bytes, stl_files, stl_candidates, updated_at
+)
+SELECT
+  ct_number, list_present, stl_present, patient_name, patient_sex, patient_age, patient_id_masked,
+  exam_date, task_created_at, exam_description, exam_device, algorithm_model, upp_status,
+  COALESCE(list_record_count, 0),
+  NULLIF(selected_list_record, '')::jsonb,
+  COALESCE(NULLIF(list_records, ''), '[]')::jsonb,
+  selected_source_case_dir, selected_source_stl_dir, processed_stl_dir, stl_case_name, stl_sequence_no,
+  COALESCE(stl_file_count, 0), COALESCE(stl_total_bytes, 0),
+  COALESCE(NULLIF(stl_files, ''), '[]')::jsonb,
+  COALESCE(NULLIF(stl_candidates, ''), '[]')::jsonb,
+  now()
+FROM stg_upp_exam_assets
+ON CONFLICT (ct_number) DO UPDATE SET
+  list_present = EXCLUDED.list_present,
+  stl_present = EXCLUDED.stl_present,
+  patient_name = EXCLUDED.patient_name,
+  patient_sex = EXCLUDED.patient_sex,
+  patient_age = EXCLUDED.patient_age,
+  patient_id_masked = EXCLUDED.patient_id_masked,
+  exam_date = EXCLUDED.exam_date,
+  task_created_at = EXCLUDED.task_created_at,
+  exam_description = EXCLUDED.exam_description,
+  exam_device = EXCLUDED.exam_device,
+  algorithm_model = EXCLUDED.algorithm_model,
+  upp_status = EXCLUDED.upp_status,
+  list_record_count = EXCLUDED.list_record_count,
+  selected_list_record = EXCLUDED.selected_list_record,
+  list_records = EXCLUDED.list_records,
+  selected_source_case_dir = EXCLUDED.selected_source_case_dir,
+  selected_source_stl_dir = EXCLUDED.selected_source_stl_dir,
+  processed_stl_dir = EXCLUDED.processed_stl_dir,
+  stl_case_name = EXCLUDED.stl_case_name,
+  stl_sequence_no = EXCLUDED.stl_sequence_no,
+  stl_file_count = EXCLUDED.stl_file_count,
+  stl_total_bytes = EXCLUDED.stl_total_bytes,
+  stl_files = EXCLUDED.stl_files,
+  stl_candidates = EXCLUDED.stl_candidates,
+  updated_at = now();
+DELETE FROM upp_stl_files WHERE ct_number IN (SELECT ct_number FROM stg_upp_exam_assets);
+INSERT INTO upp_stl_files (
+  ct_number, file_count, total_bytes, segment_names, segment_families, segment_categories, file_names, source_file_paths, processed_file_paths, files, updated_at
+)
+SELECT
+  ct_number,
+  COALESCE(file_count, 0),
+  COALESCE(total_bytes, 0),
+  COALESCE(NULLIF(segment_names, ''), '[]')::jsonb,
+  COALESCE(NULLIF(segment_families, ''), '[]')::jsonb,
+  COALESCE(NULLIF(segment_categories, ''), '[]')::jsonb,
+  COALESCE(NULLIF(file_names, ''), '[]')::jsonb,
+  COALESCE(NULLIF(source_file_paths, ''), '[]')::jsonb,
+  COALESCE(NULLIF(processed_file_paths, ''), '[]')::jsonb,
+  COALESCE(NULLIF(files, ''), '[]')::jsonb,
+  now()
+FROM stg_upp_stl_files;
+"""
+    env = os.environ.copy()
+    password = args.password or env.get("PGPASSWORD")
+    if password:
+        env["PGPASSWORD"] = password
+    command = ["psql"]
+    if args.host:
+        command.extend(["-h", args.host])
+    if args.port:
+        command.extend(["-p", str(args.port)])
+    if args.user:
+        command.extend(["-U", args.user])
+    if args.dbname:
+        command.extend(["-d", args.dbname])
+    command.extend(["-v", "ON_ERROR_STOP=1"])
+    completed = subprocess.run(command, input=sql, text=True, env=env, capture_output=True)
+    print(completed.stdout, end="")
+    if completed.returncode != 0:
+        print(completed.stderr, end="")
+        completed.check_returncode()
+
+
+def write_report(path: Path, report: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--stl-root", default=str(DEFAULT_STL_ROOT), help="待处理STL数据根目录")
+    parser.add_argument("--processed-root", default=str(DEFAULT_PROCESSED_ROOT), help="规范化后的STL输出目录")
+    parser.add_argument("--list-json", default=str(DEFAULT_LIST_JSON), help="UPP列表合并结构化JSON")
+    parser.add_argument("--schema", default=str(DEFAULT_SCHEMA), help="PostgreSQL建表SQL")
+    parser.add_argument("--report", default=str(DEFAULT_REPORT), help="同步报告JSON")
+    parser.add_argument("--link-mode", choices=["hardlink", "copy", "symlink"], default="hardlink", help="输出STL文件方式")
+    parser.add_argument("--no-refresh-files", action="store_true", help="不清空已处理CT目录中的旧文件")
+    parser.add_argument("--dry-run", action="store_true", help="只整理文件和生成报告，不写数据库")
+    parser.add_argument("--host", default=os.getenv("PGHOST", ""))
+    parser.add_argument("--port", default=os.getenv("PGPORT", ""))
+    parser.add_argument("--dbname", default=os.getenv("PGDATABASE", ""))
+    parser.add_argument("--user", default=os.getenv("PGUSER", ""))
+    parser.add_argument("--password", default=os.getenv("PGPASSWORD", ""), help="数据库密码；也可用PGPASSWORD环境变量")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    stl_root = Path(args.stl_root)
+    processed_root = Path(args.processed_root)
+    all_candidates, no_ct_dirs = scan_stl_candidates(stl_root)
+    selected = choose_candidates(all_candidates)
+    processed_files, link_methods = materialize_selected_stl(
+        selected=selected,
+        processed_root=processed_root,
+        link_mode=args.link_mode,
+        refresh_files=not args.no_refresh_files,
+    )
+    list_records, invalid_list_records = load_list_records(Path(args.list_json))
+    asset_rows, stl_rows, stl_file_count = build_rows(
+        selected=selected,
+        all_candidates=all_candidates,
+        processed_root=processed_root,
+        processed_files=processed_files,
+        list_records=list_records,
+    )
+    duplicate_ct_numbers = sorted(ct_number for ct_number, items in all_candidates.items() if len(items) > 1)
+    report = {
+        "stl_candidate_dirs": sum(len(items) for items in all_candidates.values()),
+        "stl_unique_ct": len(selected),
+        "stl_no_ct_dirs": len(no_ct_dirs),
+        "stl_duplicate_ct": len(duplicate_ct_numbers),
+        "list_unique_valid_ct": len(list_records),
+        "list_invalid_ct_records": len(invalid_list_records),
+        "asset_rows": len(asset_rows),
+        "stl_table_rows": len(stl_rows),
+        "stl_file_rows": stl_file_count,
+        "matched_list_and_stl_ct": len(set(selected) & set(list_records)),
+        "list_without_stl_ct": len(set(list_records) - set(selected)),
+        "stl_without_list_ct": len(set(selected) - set(list_records)),
+        "duplicate_ct_examples": {
+            ct_number: [item.summary() for item in sorted(all_candidates[ct_number], key=lambda item: item.score(), reverse=True)]
+            for ct_number in duplicate_ct_numbers[:20]
+        },
+        "no_ct_dir_examples": no_ct_dirs[:20],
+        "invalid_list_ct_examples": invalid_list_records[:20],
+        "link_methods": dict(sorted(link_methods.items())[:20]),
+        "processed_root": path_text(processed_root),
+        "selection_rule": "同一CT号优先选择STL文件数更多；文件数相同选择目录/病例名中的较大序号",
+        "dry_run": args.dry_run,
+    }
+    write_report(Path(args.report), report)
+    asset_csv = write_csv(asset_rows, ASSET_FIELDS, "_upp_exam_assets.csv")
+    stl_csv = write_csv(stl_rows, STL_FIELDS, "_upp_stl_files.csv")
+    try:
+        if not args.dry_run:
+            run_psql(args, asset_csv, stl_csv)
+    finally:
+        asset_csv.unlink(missing_ok=True)
+        stl_csv.unlink(missing_ok=True)
+    print(json.dumps({k: report[k] for k in [
+        "stl_candidate_dirs",
+        "stl_unique_ct",
+        "stl_no_ct_dirs",
+        "stl_duplicate_ct",
+        "list_unique_valid_ct",
+        "asset_rows",
+        "stl_table_rows",
+        "stl_file_rows",
+        "matched_list_and_stl_ct",
+        "list_without_stl_ct",
+        "stl_without_list_ct",
+    ]}, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()

UPP_数据库构建/03_统计STL名称.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""统计已处理STL名称，并按可解释的医学/结构类别归类。"""
+
+from __future__ import annotations
+
+import csv
+import json
+import re
+from collections import Counter, defaultdict
+from pathlib import Path
+from typing import Any
+
+
+BASE_DIR = Path(__file__).resolve().parents[1]
+DEFAULT_PROCESSED_ROOT = BASE_DIR / "UPP_STL处理" / "已处理STL数据"
+DEFAULT_JSON = BASE_DIR / "UPP_数据库构建" / "UPP_STL名称统计.json"
+DEFAULT_CSV = BASE_DIR / "UPP_数据库构建" / "UPP_STL名称统计.csv"
+DEFAULT_FAMILY_CSV = BASE_DIR / "UPP_数据库构建" / "UPP_STL_family统计.csv"
+DEFAULT_ORDERED_CSV = BASE_DIR / "UPP_数据库构建" / "UPP_STL文件family顺序明细.csv"
+
+
+def classify(segment_name: str) -> tuple[str, str]:
+    if segment_name in {"liver", "liver_left", "liver_right"}:
+        return "肝脏主体", segment_name
+    if re.fullmatch(r"liver_segment_S[1-8]", segment_name):
+        return "肝段", segment_name
+    if segment_name in {"liver_artery", "liver_vein", "portal_vein", "bile_duct"}:
+        return "血管胆管", segment_name
+    if segment_name in {"pancreas", "spleen", "cholecyst"}:
+        return "腹部脏器", segment_name
+    if segment_name in {"skin", "rib", "vertebrae", "sternum", "hipbone", "sacrum"}:
+        return "体表骨骼", segment_name
+    if re.fullmatch(r"liver_tumor_\d+", segment_name):
+        return "肝脏肿瘤", "liver_tumor_*"
+    if re.fullmatch(r"liver_cyst_\d+", segment_name):
+        return "肝囊肿", "liver_cyst_*"
+    if re.fullmatch(r"liver_hemangioma_\d+", segment_name):
+        return "肝血管瘤", "liver_hemangioma_*"
+    if re.fullmatch(r"pancreas_tumor_\d+", segment_name):
+        return "胰腺肿瘤", "pancreas_tumor_*"
+    if re.fullmatch(r"Segment_\d+", segment_name):
+        return "未命名分割", "Segment_*"
+    return "其他", segment_name
+
+
+def collect(processed_root: Path) -> dict[str, Any]:
+    segment_counter: Counter[str] = Counter()
+    category_counter: Counter[str] = Counter()
+    family_counter: Counter[str] = Counter()
+    family_ct: dict[str, set[str]] = defaultdict(set)
+    category_ct: dict[str, set[str]] = defaultdict(set)
+    name_ct: dict[str, set[str]] = defaultdict(set)
+    family_members: dict[str, set[str]] = defaultdict(set)
+    ordered_files: list[dict[str, Any]] = []
+    ct_dirs = [item for item in processed_root.iterdir() if item.is_dir()]
+
+    for ct_dir in sorted(ct_dirs):
+        ct_number = ct_dir.name
+        for order_no, stl_file in enumerate(sorted(ct_dir.glob("*.stl")), start=1):
+            segment_name = stl_file.stem
+            category, family = classify(segment_name)
+            segment_counter[segment_name] += 1
+            category_counter[category] += 1
+            family_counter[family] += 1
+            name_ct[segment_name].add(ct_number)
+            family_ct[family].add(ct_number)
+            category_ct[category].add(ct_number)
+            family_members[family].add(segment_name)
+            ordered_files.append(
+                {
+                    "ct_number": ct_number,
+                    "order_no": order_no,
+                    "segment_name": segment_name,
+                    "family": family,
+                    "category": category,
+                    "file_name": stl_file.name,
+                    "file_path": str(stl_file),
+                }
+            )
+
+    by_name = [
+        {
+            "segment_name": name,
+            "category": classify(name)[0],
+            "family": classify(name)[1],
+            "file_count": count,
+            "ct_count": len(name_ct[name]),
+        }
+        for name, count in segment_counter.most_common()
+    ]
+    by_category = [
+        {
+            "category": category,
+            "file_count": count,
+            "ct_count": len(category_ct[category]),
+        }
+        for category, count in category_counter.most_common()
+    ]
+    by_family = [
+        {
+            "family": family,
+            "category": classify(family.replace("*", "1"))[0] if "*" in family else classify(family)[0],
+            "file_count": count,
+            "ct_count": len(family_ct[family]),
+            "segment_name_count": len(family_members[family]),
+            "segment_names": "|".join(sorted(family_members[family])),
+        }
+        for family, count in family_counter.most_common()
+    ]
+    return {
+        "processed_root": str(processed_root.relative_to(BASE_DIR)),
+        "ct_count": len(ct_dirs),
+        "stl_file_count": sum(segment_counter.values()),
+        "unique_segment_name_count": len(segment_counter),
+        "by_category": by_category,
+        "by_family": by_family,
+        "by_name": by_name,
+        "ordered_files": ordered_files,
+    }
+
+
+def write_csv(path: Path, rows: list[dict[str, Any]], fieldnames: list[str]) -> None:
+    with path.open("w", encoding="utf-8", newline="") as file:
+        writer = csv.DictWriter(file, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def main() -> None:
+    result = collect(DEFAULT_PROCESSED_ROOT)
+    ordered_files = result.pop("ordered_files")
+    DEFAULT_JSON.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
+    write_csv(DEFAULT_CSV, result["by_name"], ["segment_name", "category", "family", "file_count", "ct_count"])
+    write_csv(
+        DEFAULT_FAMILY_CSV,
+        result["by_family"],
+        ["family", "category", "file_count", "ct_count", "segment_name_count", "segment_names"],
+    )
+    write_csv(
+        DEFAULT_ORDERED_CSV,
+        ordered_files,
+        ["ct_number", "order_no", "segment_name", "family", "category", "file_name", "file_path"],
+    )
+    print(json.dumps({
+        "ct_count": result["ct_count"],
+        "stl_file_count": result["stl_file_count"],
+        "unique_segment_name_count": result["unique_segment_name_count"],
+        "json": str(DEFAULT_JSON),
+        "csv": str(DEFAULT_CSV),
+        "family_csv": str(DEFAULT_FAMILY_CSV),
+        "ordered_csv": str(DEFAULT_ORDERED_CSV),
+    }, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()

UPP_数据库构建/README.md

@@ -0,0 +1,45 @@
+# UPP STL资产索引
+
+本目录用于把 `UPP_STL处理/待处理STL数据` 和 `UPP列表处理` 的合并 list 建成 CT 号唯一索引。
+
+处理规则：
+
+- STL 候选目录必须能从路径中提取 `CT号`，格式为 `CT...` 或 `DCT...`。
+- 同一 CT 号有多个 STL 目录时，优先选择 STL 文件数更多的目录。
+- 文件数相同，则选择病例名或 `*-STL` 目录中序号更大的目录。
+- 规范化输出到 `UPP_STL处理/已处理STL数据/<CT号>/`。
+- 数据库中 `upp_exam_assets.ct_number` 是主键，可用 list 的检查号查询对应 STL 路径；未来 CT 数据也可以继续挂在同一行。
+
+运行：
+
+```bash
+cd /home/wkmgc/Desktop/PACS数据处理
+PGHOST='<host>' PGPORT='<port>' PGDATABASE='<database>' PGUSER='<user>' PGPASSWORD='<password>' \
+  python3 UPP_数据库构建/02_同步UPP_STL资产.py
+```
+
+主要表：
+
+- `upp_exam_assets`：CT 号唯一资产索引，含 list 元数据、最终选中 STL 目录、候选目录、STL 文件清单。
+- `upp_stl_files`：最终选中 STL 的 CT 级聚合表，每个 CT 号唯一一行，文件名、分割名、family、分类、路径和完整文件清单用 JSONB 存储。
+
+STL 名称统计：
+
+```bash
+python3 UPP_数据库构建/03_统计STL名称.py
+```
+
+输出：
+
+- `UPP_数据库构建/UPP_STL名称统计.json`
+- `UPP_数据库构建/UPP_STL名称统计.csv`
+- `UPP_数据库构建/UPP_STL_family统计.csv`
+- `UPP_数据库构建/UPP_STL文件family顺序明细.csv`，含逐 CT 文件路径，默认不提交
+
+常用查询：
+
+```sql
+SELECT ct_number, patient_name, processed_stl_dir, stl_file_count
+FROM upp_exam_assets
+WHERE ct_number = '<CT号>';
+```

UPP_数据库构建/UPP_STL_family统计.csv

@@ -0,0 +1,30 @@
+family,category,file_count,ct_count,segment_name_count,segment_names
+liver_cyst_*,肝囊肿,945,272,37,liver_cyst_1|liver_cyst_10|liver_cyst_11|liver_cyst_12|liver_cyst_13|liver_cyst_14|liver_cyst_15|liver_cyst_16|liver_cyst_17|liver_cyst_18|liver_cyst_19|liver_cyst_2|liver_cyst_20|liver_cyst_21|liver_cyst_22|liver_cyst_23|liver_cyst_24|liver_cyst_25|liver_cyst_26|liver_cyst_27|liver_cyst_28|liver_cyst_29|liver_cyst_3|liver_cyst_30|liver_cyst_31|liver_cyst_32|liver_cyst_33|liver_cyst_34|liver_cyst_35|liver_cyst_36|liver_cyst_37|liver_cyst_4|liver_cyst_5|liver_cyst_6|liver_cyst_7|liver_cyst_8|liver_cyst_9
+liver_tumor_*,肝脏肿瘤,839,576,12,liver_tumor_1|liver_tumor_10|liver_tumor_11|liver_tumor_12|liver_tumor_2|liver_tumor_3|liver_tumor_4|liver_tumor_5|liver_tumor_6|liver_tumor_7|liver_tumor_8|liver_tumor_9
+liver,肝脏主体,725,725,1,liver
+liver_left,肝脏主体,725,725,1,liver_left
+liver_right,肝脏主体,725,725,1,liver_right
+liver_segment_S1,肝段,725,725,1,liver_segment_S1
+liver_segment_S2,肝段,725,725,1,liver_segment_S2
+liver_segment_S3,肝段,725,725,1,liver_segment_S3
+liver_segment_S4,肝段,725,725,1,liver_segment_S4
+liver_segment_S5,肝段,725,725,1,liver_segment_S5
+liver_segment_S6,肝段,725,725,1,liver_segment_S6
+liver_segment_S7,肝段,725,725,1,liver_segment_S7
+liver_segment_S8,肝段,725,725,1,liver_segment_S8
+skin,体表骨骼,725,725,1,skin
+liver_artery,血管胆管,724,724,1,liver_artery
+portal_vein,血管胆管,724,724,1,portal_vein
+rib,体表骨骼,724,724,1,rib
+vertebrae,体表骨骼,724,724,1,vertebrae
+liver_vein,血管胆管,723,723,1,liver_vein
+pancreas,腹部脏器,723,723,1,pancreas
+bile_duct,血管胆管,720,720,1,bile_duct
+spleen,腹部脏器,717,717,1,spleen
+sternum,体表骨骼,684,684,1,sternum
+cholecyst,腹部脏器,565,565,1,cholecyst
+hipbone,体表骨骼,455,455,1,hipbone
+sacrum,体表骨骼,445,445,1,sacrum
+liver_hemangioma_*,肝血管瘤,74,43,5,liver_hemangioma_1|liver_hemangioma_2|liver_hemangioma_3|liver_hemangioma_4|liver_hemangioma_5
+pancreas_tumor_*,胰腺肿瘤,12,12,1,pancreas_tumor_1
+Segment_*,未命名分割,9,5,4,Segment_0|Segment_1|Segment_2|Segment_3

UPP_数据库构建/UPP_STL名称统计.csv

@@ -0,0 +1,84 @@
+segment_name,category,family,file_count,ct_count
+liver,肝脏主体,liver,725,725
+liver_left,肝脏主体,liver_left,725,725
+liver_right,肝脏主体,liver_right,725,725
+liver_segment_S1,肝段,liver_segment_S1,725,725
+liver_segment_S2,肝段,liver_segment_S2,725,725
+liver_segment_S3,肝段,liver_segment_S3,725,725
+liver_segment_S4,肝段,liver_segment_S4,725,725
+liver_segment_S5,肝段,liver_segment_S5,725,725
+liver_segment_S6,肝段,liver_segment_S6,725,725
+liver_segment_S7,肝段,liver_segment_S7,725,725
+liver_segment_S8,肝段,liver_segment_S8,725,725
+skin,体表骨骼,skin,725,725
+liver_artery,血管胆管,liver_artery,724,724
+portal_vein,血管胆管,portal_vein,724,724
+rib,体表骨骼,rib,724,724
+vertebrae,体表骨骼,vertebrae,724,724
+liver_vein,血管胆管,liver_vein,723,723
+pancreas,腹部脏器,pancreas,723,723
+bile_duct,血管胆管,bile_duct,720,720
+spleen,腹部脏器,spleen,717,717
+sternum,体表骨骼,sternum,684,684
+liver_tumor_1,肝脏肿瘤,liver_tumor_*,576,576
+cholecyst,腹部脏器,cholecyst,565,565
+hipbone,体表骨骼,hipbone,455,455
+sacrum,体表骨骼,sacrum,445,445
+liver_cyst_1,肝囊肿,liver_cyst_*,272,272
+liver_cyst_2,肝囊肿,liver_cyst_*,154,154
+liver_tumor_2,肝脏肿瘤,liver_tumor_*,143,143
+liver_cyst_3,肝囊肿,liver_cyst_*,102,102
+liver_cyst_4,肝囊肿,liver_cyst_*,70,70
+liver_cyst_5,肝囊肿,liver_cyst_*,59,59
+liver_tumor_3,肝脏肿瘤,liver_tumor_*,50,50
+liver_cyst_6,肝囊肿,liver_cyst_*,49,49
+liver_hemangioma_1,肝血管瘤,liver_hemangioma_*,43,43
+liver_cyst_7,肝囊肿,liver_cyst_*,36,36
+liver_cyst_8,肝囊肿,liver_cyst_*,28,28
+liver_tumor_4,肝脏肿瘤,liver_tumor_*,25,25
+liver_cyst_9,肝囊肿,liver_cyst_*,23,23
+liver_cyst_10,肝囊肿,liver_cyst_*,20,20
+liver_hemangioma_2,肝血管瘤,liver_hemangioma_*,16,16
+liver_cyst_11,肝囊肿,liver_cyst_*,15,15
+liver_cyst_12,肝囊肿,liver_cyst_*,12,12
+liver_cyst_13,肝囊肿,liver_cyst_*,12,12
+pancreas_tumor_1,胰腺肿瘤,pancreas_tumor_*,12,12
+liver_tumor_5,肝脏肿瘤,liver_tumor_*,12,12
+liver_cyst_14,肝囊肿,liver_cyst_*,11,11
+liver_tumor_6,肝脏肿瘤,liver_tumor_*,10,10
+liver_cyst_15,肝囊肿,liver_cyst_*,9,9
+liver_hemangioma_3,肝血管瘤,liver_hemangioma_*,9,9
+liver_tumor_7,肝脏肿瘤,liver_tumor_*,9,9
+liver_cyst_16,肝囊肿,liver_cyst_*,8,8
+liver_cyst_17,肝囊肿,liver_cyst_*,7,7
+liver_cyst_18,肝囊肿,liver_cyst_*,6,6
+liver_cyst_19,肝囊肿,liver_cyst_*,6,6
+liver_cyst_20,肝囊肿,liver_cyst_*,6,6
+liver_cyst_21,肝囊肿,liver_cyst_*,5,5
+liver_tumor_8,肝脏肿瘤,liver_tumor_*,5,5
+liver_hemangioma_4,肝血管瘤,liver_hemangioma_*,4,4
+liver_cyst_22,肝囊肿,liver_cyst_*,4,4
+liver_cyst_23,肝囊肿,liver_cyst_*,4,4
+liver_cyst_24,肝囊肿,liver_cyst_*,4,4
+liver_cyst_25,肝囊肿,liver_cyst_*,4,4
+Segment_0,未命名分割,Segment_*,4,4
+liver_tumor_9,肝脏肿瘤,liver_tumor_*,4,4
+liver_cyst_26,肝囊肿,liver_cyst_*,3,3
+liver_cyst_27,肝囊肿,liver_cyst_*,3,3
+liver_cyst_28,肝囊肿,liver_cyst_*,3,3
+liver_tumor_10,肝脏肿瘤,liver_tumor_*,3,3
+liver_hemangioma_5,肝血管瘤,liver_hemangioma_*,2,2
+liver_cyst_29,肝囊肿,liver_cyst_*,2,2
+Segment_1,未命名分割,Segment_*,2,2
+Segment_2,未命名分割,Segment_*,2,2
+liver_cyst_30,肝囊肿,liver_cyst_*,1,1
+liver_cyst_31,肝囊肿,liver_cyst_*,1,1
+liver_cyst_32,肝囊肿,liver_cyst_*,1,1
+liver_cyst_33,肝囊肿,liver_cyst_*,1,1
+liver_cyst_34,肝囊肿,liver_cyst_*,1,1
+liver_cyst_35,肝囊肿,liver_cyst_*,1,1
+liver_cyst_36,肝囊肿,liver_cyst_*,1,1
+liver_cyst_37,肝囊肿,liver_cyst_*,1,1
+Segment_3,未命名分割,Segment_*,1,1
+liver_tumor_11,肝脏肿瘤,liver_tumor_*,1,1
+liver_tumor_12,肝脏肿瘤,liver_tumor_*,1,1

UPP_数据库构建/UPP_STL名称统计.json

@@ -0,0 +1,875 @@
+{
+  "processed_root": "UPP_STL处理/已处理STL数据",
+  "ct_count": 726,
+  "stl_file_count": 18507,
+  "unique_segment_name_count": 83,
+  "by_category": [
+    {
+      "category": "肝段",
+      "file_count": 5800,
+      "ct_count": 725
+    },
+    {
+      "category": "体表骨骼",
+      "file_count": 3757,
+      "ct_count": 726
+    },
+    {
+      "category": "血管胆管",
+      "file_count": 2891,
+      "ct_count": 724
+    },
+    {
+      "category": "肝脏主体",
+      "file_count": 2175,
+      "ct_count": 725
+    },
+    {
+      "category": "腹部脏器",
+      "file_count": 2005,
+      "ct_count": 724
+    },
+    {
+      "category": "肝囊肿",
+      "file_count": 945,
+      "ct_count": 272
+    },
+    {
+      "category": "肝脏肿瘤",
+      "file_count": 839,
+      "ct_count": 576
+    },
+    {
+      "category": "肝血管瘤",
+      "file_count": 74,
+      "ct_count": 43
+    },
+    {
+      "category": "胰腺肿瘤",
+      "file_count": 12,
+      "ct_count": 12
+    },
+    {
+      "category": "未命名分割",
+      "file_count": 9,
+      "ct_count": 5
+    }
+  ],
+  "by_family": [
+    {
+      "family": "liver_cyst_*",
+      "category": "肝囊肿",
+      "file_count": 945,
+      "ct_count": 272,
+      "segment_name_count": 37,
+      "segment_names": "liver_cyst_1|liver_cyst_10|liver_cyst_11|liver_cyst_12|liver_cyst_13|liver_cyst_14|liver_cyst_15|liver_cyst_16|liver_cyst_17|liver_cyst_18|liver_cyst_19|liver_cyst_2|liver_cyst_20|liver_cyst_21|liver_cyst_22|liver_cyst_23|liver_cyst_24|liver_cyst_25|liver_cyst_26|liver_cyst_27|liver_cyst_28|liver_cyst_29|liver_cyst_3|liver_cyst_30|liver_cyst_31|liver_cyst_32|liver_cyst_33|liver_cyst_34|liver_cyst_35|liver_cyst_36|liver_cyst_37|liver_cyst_4|liver_cyst_5|liver_cyst_6|liver_cyst_7|liver_cyst_8|liver_cyst_9"
+    },
+    {
+      "family": "liver_tumor_*",
+      "category": "肝脏肿瘤",
+      "file_count": 839,
+      "ct_count": 576,
+      "segment_name_count": 12,
+      "segment_names": "liver_tumor_1|liver_tumor_10|liver_tumor_11|liver_tumor_12|liver_tumor_2|liver_tumor_3|liver_tumor_4|liver_tumor_5|liver_tumor_6|liver_tumor_7|liver_tumor_8|liver_tumor_9"
+    },
+    {
+      "family": "liver",
+      "category": "肝脏主体",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver"
+    },
+    {
+      "family": "liver_left",
+      "category": "肝脏主体",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_left"
+    },
+    {
+      "family": "liver_right",
+      "category": "肝脏主体",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_right"
+    },
+    {
+      "family": "liver_segment_S1",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S1"
+    },
+    {
+      "family": "liver_segment_S2",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S2"
+    },
+    {
+      "family": "liver_segment_S3",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S3"
+    },
+    {
+      "family": "liver_segment_S4",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S4"
+    },
+    {
+      "family": "liver_segment_S5",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S5"
+    },
+    {
+      "family": "liver_segment_S6",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S6"
+    },
+    {
+      "family": "liver_segment_S7",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S7"
+    },
+    {
+      "family": "liver_segment_S8",
+      "category": "肝段",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "liver_segment_S8"
+    },
+    {
+      "family": "skin",
+      "category": "体表骨骼",
+      "file_count": 725,
+      "ct_count": 725,
+      "segment_name_count": 1,
+      "segment_names": "skin"
+    },
+    {
+      "family": "liver_artery",
+      "category": "血管胆管",
+      "file_count": 724,
+      "ct_count": 724,
+      "segment_name_count": 1,
+      "segment_names": "liver_artery"
+    },
+    {
+      "family": "portal_vein",
+      "category": "血管胆管",
+      "file_count": 724,
+      "ct_count": 724,
+      "segment_name_count": 1,
+      "segment_names": "portal_vein"
+    },
+    {
+      "family": "rib",
+      "category": "体表骨骼",
+      "file_count": 724,
+      "ct_count": 724,
+      "segment_name_count": 1,
+      "segment_names": "rib"
+    },
+    {
+      "family": "vertebrae",
+      "category": "体表骨骼",
+      "file_count": 724,
+      "ct_count": 724,
+      "segment_name_count": 1,
+      "segment_names": "vertebrae"
+    },
+    {
+      "family": "liver_vein",
+      "category": "血管胆管",
+      "file_count": 723,
+      "ct_count": 723,
+      "segment_name_count": 1,
+      "segment_names": "liver_vein"
+    },
+    {
+      "family": "pancreas",
+      "category": "腹部脏器",
+      "file_count": 723,
+      "ct_count": 723,
+      "segment_name_count": 1,
+      "segment_names": "pancreas"
+    },
+    {
+      "family": "bile_duct",
+      "category": "血管胆管",
+      "file_count": 720,
+      "ct_count": 720,
+      "segment_name_count": 1,
+      "segment_names": "bile_duct"
+    },
+    {
+      "family": "spleen",
+      "category": "腹部脏器",
+      "file_count": 717,
+      "ct_count": 717,
+      "segment_name_count": 1,
+      "segment_names": "spleen"
+    },
+    {
+      "family": "sternum",
+      "category": "体表骨骼",
+      "file_count": 684,
+      "ct_count": 684,
+      "segment_name_count": 1,
+      "segment_names": "sternum"
+    },
+    {
+      "family": "cholecyst",
+      "category": "腹部脏器",
+      "file_count": 565,
+      "ct_count": 565,
+      "segment_name_count": 1,
+      "segment_names": "cholecyst"
+    },
+    {
+      "family": "hipbone",
+      "category": "体表骨骼",
+      "file_count": 455,
+      "ct_count": 455,
+      "segment_name_count": 1,
+      "segment_names": "hipbone"
+    },
+    {
+      "family": "sacrum",
+      "category": "体表骨骼",
+      "file_count": 445,
+      "ct_count": 445,
+      "segment_name_count": 1,
+      "segment_names": "sacrum"
+    },
+    {
+      "family": "liver_hemangioma_*",
+      "category": "肝血管瘤",
+      "file_count": 74,
+      "ct_count": 43,
+      "segment_name_count": 5,
+      "segment_names": "liver_hemangioma_1|liver_hemangioma_2|liver_hemangioma_3|liver_hemangioma_4|liver_hemangioma_5"
+    },
+    {
+      "family": "pancreas_tumor_*",
+      "category": "胰腺肿瘤",
+      "file_count": 12,
+      "ct_count": 12,
+      "segment_name_count": 1,
+      "segment_names": "pancreas_tumor_1"
+    },
+    {
+      "family": "Segment_*",
+      "category": "未命名分割",
+      "file_count": 9,
+      "ct_count": 5,
+      "segment_name_count": 4,
+      "segment_names": "Segment_0|Segment_1|Segment_2|Segment_3"
+    }
+  ],
+  "by_name": [
+    {
+      "segment_name": "liver",
+      "category": "肝脏主体",
+      "family": "liver",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_left",
+      "category": "肝脏主体",
+      "family": "liver_left",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_right",
+      "category": "肝脏主体",
+      "family": "liver_right",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S1",
+      "category": "肝段",
+      "family": "liver_segment_S1",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S2",
+      "category": "肝段",
+      "family": "liver_segment_S2",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S3",
+      "category": "肝段",
+      "family": "liver_segment_S3",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S4",
+      "category": "肝段",
+      "family": "liver_segment_S4",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S5",
+      "category": "肝段",
+      "family": "liver_segment_S5",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S6",
+      "category": "肝段",
+      "family": "liver_segment_S6",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S7",
+      "category": "肝段",
+      "family": "liver_segment_S7",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_segment_S8",
+      "category": "肝段",
+      "family": "liver_segment_S8",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "skin",
+      "category": "体表骨骼",
+      "family": "skin",
+      "file_count": 725,
+      "ct_count": 725
+    },
+    {
+      "segment_name": "liver_artery",
+      "category": "血管胆管",
+      "family": "liver_artery",
+      "file_count": 724,
+      "ct_count": 724
+    },
+    {
+      "segment_name": "portal_vein",
+      "category": "血管胆管",
+      "family": "portal_vein",
+      "file_count": 724,
+      "ct_count": 724
+    },
+    {
+      "segment_name": "rib",
+      "category": "体表骨骼",
+      "family": "rib",
+      "file_count": 724,
+      "ct_count": 724
+    },
+    {
+      "segment_name": "vertebrae",
+      "category": "体表骨骼",
+      "family": "vertebrae",
+      "file_count": 724,
+      "ct_count": 724
+    },
+    {
+      "segment_name": "liver_vein",
+      "category": "血管胆管",
+      "family": "liver_vein",
+      "file_count": 723,
+      "ct_count": 723
+    },
+    {
+      "segment_name": "pancreas",
+      "category": "腹部脏器",
+      "family": "pancreas",
+      "file_count": 723,
+      "ct_count": 723
+    },
+    {
+      "segment_name": "bile_duct",
+      "category": "血管胆管",
+      "family": "bile_duct",
+      "file_count": 720,
+      "ct_count": 720
+    },
+    {
+      "segment_name": "spleen",
+      "category": "腹部脏器",
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