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Refine PACS viewer annotation workflow

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Codex
2026-05-27 10:25:33 +08:00
parent 9c478ed392
commit ac75c37e3f
5 changed files with 971 additions and 161 deletions
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10
PACS_DICOM处理/数据处理网页端/README.md
View File

@@ -38,13 +38,15 @@ uvicorn app:app --host 127.0.0.1 --port 8107
- 左侧检查列表：来自 PostgreSQL `pacs_dicom_files`。
- 中间序列列表：从已处理 DICOM 目录扫描 DICOM 头信息。
- 右侧查看器：支持横断面、矢状面、冠状面，窗宽窗位、旋转、切片进度条。
- 右侧查看器：支持轴位原始、矢状位重建、冠状位重建，窗宽窗位、旋转、切片进度条。
- 影像操作：支持鼠标滚轮缩放、拖拽平移、按钮缩放和复位。
- 图像叠层：可显示/隐藏患者、检查、方向、张数、窗宽窗位和缩放信息。
- DICOM 信息：查看患者、检查、序列、像素间距、切片间距等元数据。
- 序列标注：选择略过、头颈部、胸部、上腹部、下腹部、盆腔；上腹部需继续选择动脉期、门静脉期或无法判别。
- AI 识别：配置 Kimi 后，可把序列张数及横断面、矢状面、冠状面代表图像传入 AI，自动给出部位、期相和备注建议。
- 序列列表：默认按拍摄时间升序排序，可切换降序；少于 80 张且未标注部位的序列默认略过。
- 序列标注：选择略过、平扫 CT、头颈部、胸部、上腹部、下腹部、盆腔；点击后自动保存；上腹部需继续选择动脉期、门静脉期或无法判别。
- AI 识别：配置 Kimi 后，可把序列张数及轴位原始、矢状位重建、冠状位重建代表图像传入 AI，自动给出部位、期相和备注建议。
- 设置：支持用户创建、角色权限展示、数据库状态和 AI 配置查看。
- 标注写入 PostgreSQL 表 `pacs_dicom_series_annotations`。
- 标注写入 PostgreSQL 表 `pacs_dicom_series_annotations`，人工标注和 AI 标注分别记录。
## 数据安全

524
PACS_DICOM处理/数据处理网页端/app.py
View File

@@ -76,7 +76,7 @@ app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
TOKENS: dict[str, str] = {}
STUDY_CACHE: dict[str, dict[str, Any]] = {}
STACK_CACHE: dict[str, tuple[float, np.ndarray]] = {}
STACK_CACHE: dict[str, tuple[float, dict[str, Any]]] = {}
class LoginIn(BaseModel):
@@ -86,9 +86,17 @@ class LoginIn(BaseModel):
class AnnotationIn(BaseModel):
body_parts: list[str] = []
manual_body_parts: list[str] = []
ai_body_parts: list[str] = []
upper_abdomen_phase: str = ""
manual_upper_abdomen_phase: str = ""
ai_upper_abdomen_phase: str = ""
plain_ct: bool = False
manual_plain_ct: bool | None = None
ai_plain_ct: bool | None = None
notes: str = ""
skipped: bool = False
ai_skipped: bool = False
class AIRequest(BaseModel):
@@ -174,8 +182,16 @@ def ensure_annotation_table() -> None:
series_instance_uid text NOT NULL,
series_description text,
body_parts jsonb NOT NULL DEFAULT '[]'::jsonb,
manual_body_parts jsonb NOT NULL DEFAULT '[]'::jsonb,
ai_body_parts jsonb NOT NULL DEFAULT '[]'::jsonb,
upper_abdomen_phase text NOT NULL DEFAULT '',
manual_upper_abdomen_phase text NOT NULL DEFAULT '',
ai_upper_abdomen_phase text NOT NULL DEFAULT '',
plain_ct boolean NOT NULL DEFAULT false,
manual_plain_ct boolean,
ai_plain_ct boolean,
skipped boolean NOT NULL DEFAULT false,
ai_skipped boolean NOT NULL DEFAULT false,
notes text NOT NULL DEFAULT '',
ai_result jsonb,
ai_model text,
@@ -184,12 +200,34 @@ def ensure_annotation_table() -> None:
updated_at timestamptz NOT NULL DEFAULT now(),
PRIMARY KEY (ct_number, series_instance_uid)
);
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS manual_body_parts jsonb NOT NULL DEFAULT '[]'::jsonb;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS ai_body_parts jsonb NOT NULL DEFAULT '[]'::jsonb;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS manual_upper_abdomen_phase text NOT NULL DEFAULT '';
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS ai_upper_abdomen_phase text NOT NULL DEFAULT '';
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS plain_ct boolean NOT NULL DEFAULT false;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS manual_plain_ct boolean;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS ai_plain_ct boolean;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS skipped boolean NOT NULL DEFAULT false;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS ai_skipped boolean NOT NULL DEFAULT false;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS ai_result jsonb;
ALTER TABLE public.pacs_dicom_series_annotations
ADD COLUMN IF NOT EXISTS ai_model text;
UPDATE public.pacs_dicom_series_annotations
SET manual_body_parts = body_parts,
manual_upper_abdomen_phase = upper_abdomen_phase
WHERE jsonb_array_length(body_parts) > 0
AND jsonb_array_length(manual_body_parts) = 0
AND jsonb_array_length(ai_body_parts) = 0;
"""
pg_scalar(sql)
@@ -372,6 +410,13 @@ def read_header(path: Path) -> dict[str, str]:
tags = [
"SeriesInstanceUID",
"StudyInstanceUID",
"AccessionNumber",
"PatientName",
"PatientID",
"PatientBirthDate",
"PatientSex",
"InstitutionName",
"StudyDate",
"SeriesNumber",
"SeriesDescription",
"InstanceNumber",
@@ -414,11 +459,101 @@ def sort_key(item: tuple[Path, dict[str, str]]) -> tuple[float, float, str]:
return (z, instance, str(path))
def valid_parts(parts: Any) -> list[str]:
if not isinstance(parts, list):
return []
return [part for part in parts if part in BODY_PARTS]
def valid_phase(value: Any) -> str:
return value if value in PHASES else ""
def bool_or_none(value: Any) -> bool | None:
if value is None:
return None
if isinstance(value, bool):
return value
if isinstance(value, str):
if value.lower() in {"true", "t", "1", "yes", "y"}:
return True
if value.lower() in {"false", "f", "0", "no", "n"}:
return False
return bool(value)
def merge_parts(manual_parts: list[str], ai_parts: list[str], skipped: bool) -> list[str]:
if skipped:
return []
merged: list[str] = []
for part in manual_parts + ai_parts:
if part in BODY_PARTS and part not in merged:
merged.append(part)
return merged
def effective_phase(manual_phase: str, ai_phase: str, body_parts: list[str], skipped: bool) -> str:
if skipped or "upper_abdomen" not in body_parts:
return ""
return valid_phase(manual_phase) or valid_phase(ai_phase)
def effective_plain_ct(manual_plain_ct: bool | None, ai_plain_ct: bool | None, fallback: bool, skipped: bool) -> bool:
if skipped:
return False
if manual_plain_ct is not None:
return manual_plain_ct
if ai_plain_ct is not None:
return ai_plain_ct
return bool(fallback)
def normalize_annotation(row: dict[str, Any] | None, default_skipped: bool = False) -> dict[str, Any]:
row = row or {}
manual_parts = valid_parts(row.get("manual_body_parts") or [])
ai_parts = valid_parts(row.get("ai_body_parts") or [])
if not manual_parts and not ai_parts:
manual_parts = valid_parts(row.get("body_parts") or [])
skipped = bool(row.get("skipped", default_skipped))
ai_skipped = bool(row.get("ai_skipped", False))
manual_phase = valid_phase(row.get("manual_upper_abdomen_phase") or "")
ai_phase = valid_phase(row.get("ai_upper_abdomen_phase") or "")
if not manual_phase and not ai_phase:
manual_phase = valid_phase(row.get("upper_abdomen_phase") or "")
body_parts = merge_parts(manual_parts, ai_parts, skipped)
plain_ct = effective_plain_ct(
bool_or_none(row.get("manual_plain_ct")),
bool_or_none(row.get("ai_plain_ct")),
bool(row.get("plain_ct", False)),
skipped,
)
return {
"body_parts": body_parts,
"manual_body_parts": [] if skipped else manual_parts,
"ai_body_parts": [] if skipped else ai_parts,
"upper_abdomen_phase": effective_phase(manual_phase, ai_phase, body_parts, skipped),
"manual_upper_abdomen_phase": "" if skipped else manual_phase,
"ai_upper_abdomen_phase": "" if skipped else ai_phase,
"plain_ct": plain_ct,
"manual_plain_ct": None if skipped else bool_or_none(row.get("manual_plain_ct")),
"ai_plain_ct": None if skipped else bool_or_none(row.get("ai_plain_ct")),
"skipped": skipped,
"ai_skipped": ai_skipped,
"notes": row.get("notes", ""),
"updated_at": row.get("updated_at", ""),
"ai_model": row.get("ai_model", ""),
}
def get_annotations(ct_number: str) -> dict[str, dict[str, Any]]:
try:
ensure_annotation_table()
rows = pg_json_rows(
f"""
SELECT series_instance_uid, body_parts, upper_abdomen_phase, skipped, notes, updated_at, ai_model
SELECT
series_instance_uid, body_parts, manual_body_parts, ai_body_parts,
upper_abdomen_phase, manual_upper_abdomen_phase, ai_upper_abdomen_phase,
plain_ct, manual_plain_ct, ai_plain_ct, skipped, ai_skipped, notes, updated_at, ai_model
FROM public.pacs_dicom_series_annotations
WHERE ct_number = {sql_literal(ct_number)}
"""
@@ -450,17 +585,35 @@ def scan_study(ct_number: str) -> dict[str, Any]:
annotations = get_annotations(ct_number)
series_list = []
file_map = {}
default_skip_rows = []
for uid, items in grouped.items():
items.sort(key=sort_key)
first = items[0][1]
last = items[-1][1]
file_map[uid] = [path for path, _ in items]
annotation = annotations.get(uid, {})
raw_annotation = annotations.get(uid)
annotation = normalize_annotation(raw_annotation)
default_skipped = len(items) < 80 and not annotation.get("body_parts") and not annotation.get("plain_ct")
if default_skipped:
annotation["skipped"] = True
annotation["manual_body_parts"] = []
annotation["ai_body_parts"] = []
annotation["body_parts"] = []
annotation["plain_ct"] = False
if default_skipped:
default_skip_rows.append((uid, first, len(items)))
series_list.append(
{
"ct_number": ct_number,
"series_uid": uid,
"study_uid": first.get("StudyInstanceUID", ""),
"accession_number": first.get("AccessionNumber", ""),
"patient_name": first.get("PatientName", ""),
"patient_id": first.get("PatientID", ""),
"patient_birth_date": first.get("PatientBirthDate", ""),
"patient_sex": first.get("PatientSex", ""),
"institution_name": first.get("InstitutionName", ""),
"study_date": first.get("StudyDate", "") or study.get("study_date", ""),
"series_number": first.get("SeriesNumber", ""),
"description": first.get("SeriesDescription", "") or "未命名序列",
"count": len(items),
@@ -476,24 +629,75 @@ def scan_study(ct_number: str) -> dict[str, Any]:
"pixel_spacing": first.get("PixelSpacing", ""),
"slice_thickness": first.get("SliceThickness", ""),
"spacing_between_slices": first.get("SpacingBetweenSlices", ""),
"annotation": {
"body_parts": annotation.get("body_parts", []),
"upper_abdomen_phase": annotation.get("upper_abdomen_phase", ""),
"skipped": bool(annotation.get("skipped", False)),
"notes": annotation.get("notes", ""),
"updated_at": annotation.get("updated_at", ""),
"ai_model": annotation.get("ai_model", ""),
},
"window_center": first.get("WindowCenter", ""),
"window_width": first.get("WindowWidth", ""),
"default_skipped": default_skipped,
"annotation": annotation,
}
)
for uid, first, count in default_skip_rows:
try:
pg_scalar(
f"""
INSERT INTO public.pacs_dicom_series_annotations (
ct_number, study_instance_uid, series_instance_uid, series_description,
body_parts, manual_body_parts, ai_body_parts, upper_abdomen_phase,
plain_ct, skipped, notes, updated_by, updated_at
)
VALUES (
{sql_literal(ct_number)},
{sql_literal(first.get('StudyInstanceUID', ''))},
{sql_literal(uid)},
{sql_literal(first.get('SeriesDescription', '') or '未命名序列')},
'[]'::jsonb,
'[]'::jsonb,
'[]'::jsonb,
'',
false,
true,
{sql_literal(f'少于80张默认略过（{count}张）')},
'system',
now()
)
ON CONFLICT (ct_number, series_instance_uid) DO UPDATE SET
skipped = true,
body_parts = '[]'::jsonb,
manual_body_parts = '[]'::jsonb,
ai_body_parts = '[]'::jsonb,
upper_abdomen_phase = '',
manual_upper_abdomen_phase = '',
ai_upper_abdomen_phase = '',
plain_ct = false,
notes = CASE
WHEN pacs_dicom_series_annotations.notes = ''
THEN EXCLUDED.notes
ELSE pacs_dicom_series_annotations.notes
END,
updated_by = 'system',
updated_at = now()
WHERE jsonb_array_length(pacs_dicom_series_annotations.body_parts) = 0
AND pacs_dicom_series_annotations.plain_ct IS NOT TRUE
""",
timeout=8,
)
except Exception:
pass
def series_time_key(row: dict[str, Any]) -> tuple[str, int, str]:
return (
row.get("series_time") or row.get("first_time") or row.get("study_time") or "",
numeric(row.get("series_number", "")),
row.get("description", ""),
)
def numeric(value: str) -> int:
try:
return int(float(value))
except Exception:
return 999999
series_list.sort(key=lambda row: (1 if row["annotation"].get("skipped") else 0, numeric(row["series_number"]), row["description"]))
series_list.sort(key=lambda row: (1 if row["annotation"].get("skipped") else 0, *series_time_key(row)))
cached = {"cached_at": time.time(), "study": study, "series": series_list, "files": file_map}
STUDY_CACHE[ct_number] = cached
return cached
@@ -535,13 +739,91 @@ def dicom_to_hu(ds: pydicom.Dataset) -> np.ndarray:
return arr * slope + intercept
def render_array(arr: np.ndarray, center: float, width: float, invert: bool = False, rotate: int = 0, max_size: int = 900) -> bytes:
def as_float(value: Any, default: float = 1.0) -> float:
try:
return float(value)
except Exception:
return default
def pixel_spacing_from_ds(ds: pydicom.Dataset) -> tuple[float, float]:
spacing = getattr(ds, "PixelSpacing", None)
if spacing and len(spacing) >= 2:
row_spacing = as_float(spacing[0], 1.0)
col_spacing = as_float(spacing[1], row_spacing)
return max(row_spacing, 0.001), max(col_spacing, 0.001)
return 1.0, 1.0
def slice_spacing_from_datasets(datasets: list[pydicom.Dataset]) -> float:
distances = []
positions = []
for ds in datasets:
position = getattr(ds, "ImagePositionPatient", None)
if position and len(position) >= 3:
positions.append(np.array([as_float(position[0]), as_float(position[1]), as_float(position[2])], dtype=np.float32))
for first, second in zip(positions, positions[1:]):
distance = float(np.linalg.norm(second - first))
if distance > 0.001:
distances.append(distance)
if distances:
return max(float(np.median(distances)), 0.001)
locations = []
for ds in datasets:
value = getattr(ds, "SliceLocation", None)
if value is not None:
locations.append(as_float(value, 0.0))
for first, second in zip(locations, locations[1:]):
distance = abs(second - first)
if distance > 0.001:
distances.append(distance)
if distances:
return max(float(np.median(distances)), 0.001)
sample = datasets[0] if datasets else None
if sample is not None:
spacing = as_float(getattr(sample, "SpacingBetweenSlices", 0), 0.0)
if spacing > 0.001:
return spacing
thickness = as_float(getattr(sample, "SliceThickness", 0), 0.0)
if thickness > 0.001:
return thickness
return 1.0
def resize_for_spacing(pil: Image.Image, row_spacing: float, col_spacing: float) -> Image.Image:
row_spacing = max(row_spacing, 0.001)
col_spacing = max(col_spacing, 0.001)
if abs(row_spacing - col_spacing) < 0.01:
return pil
base = min(row_spacing, col_spacing)
target_w = max(1, int(round(pil.width * col_spacing / base)))
target_h = max(1, int(round(pil.height * row_spacing / base)))
max_edge = 2200
scale = min(1.0, max_edge / max(target_w, target_h))
target_size = (max(1, int(round(target_w * scale))), max(1, int(round(target_h * scale))))
if target_size == pil.size:
return pil
return pil.resize(target_size, Image.Resampling.BILINEAR)
def render_array(
arr: np.ndarray,
center: float,
width: float,
invert: bool = False,
rotate: int = 0,
max_size: int = 900,
pixel_spacing: tuple[float, float] = (1.0, 1.0),
) -> bytes:
low = center - width / 2.0
high = center + width / 2.0
img = ((np.clip(arr, low, high) - low) / max(high - low, 1.0) * 255.0).astype(np.uint8)
if invert:
img = 255 - img
pil = Image.fromarray(img)
pil = resize_for_spacing(pil, pixel_spacing[0], pixel_spacing[1])
if rotate:
pil = pil.rotate(-rotate, expand=True)
if max(pil.size) > max_size:
@@ -551,7 +833,7 @@ def render_array(arr: np.ndarray, center: float, width: float, invert: bool = Fa
return output.getvalue()
def load_stack(ct_number: str, series_uid: str) -> np.ndarray:
def load_stack_data(ct_number: str, series_uid: str) -> dict[str, Any]:
key = f"{ct_number}|{series_uid}"
cached = STACK_CACHE.get(key)
if cached:
@@ -559,15 +841,24 @@ def load_stack(ct_number: str, series_uid: str) -> np.ndarray:
return cached[1]
files = get_series_files(ct_number, series_uid)
arrays = []
datasets = []
for path in files:
ds = pydicom.dcmread(str(path), force=True)
datasets.append(ds)
arrays.append(dicom_to_hu(ds))
stack = np.stack(arrays, axis=0)
STACK_CACHE[key] = (time.time(), stack)
row_spacing, col_spacing = pixel_spacing_from_ds(datasets[min(len(datasets) - 1, len(datasets) // 2)])
payload = {
"stack": stack,
"row_spacing": row_spacing,
"col_spacing": col_spacing,
"slice_spacing": slice_spacing_from_datasets(datasets),
}
STACK_CACHE[key] = (time.time(), payload)
if len(STACK_CACHE) > 2:
oldest = sorted(STACK_CACHE.items(), key=lambda item: item[1][0])[0][0]
STACK_CACHE.pop(oldest, None)
return stack
return payload
@app.get("/api/image")
@@ -586,21 +877,31 @@ def image(
index = min(index, len(files) - 1)
ds = pydicom.dcmread(str(files[index]), force=True)
center, width = window_values(ds, window)
payload = render_array(dicom_to_hu(ds), center, width, getattr(ds, "PhotometricInterpretation", "") == "MONOCHROME1", rotate)
payload = render_array(
dicom_to_hu(ds),
center,
width,
getattr(ds, "PhotometricInterpretation", "") == "MONOCHROME1",
rotate,
pixel_spacing=pixel_spacing_from_ds(ds),
)
return Response(payload, media_type="image/png")
stack = load_stack(ct_number, series_uid)
stack_data = load_stack_data(ct_number, series_uid)
stack = stack_data["stack"]
sample_ds = pydicom.dcmread(str(files[min(len(files) - 1, len(files) // 2)]), stop_before_pixels=True, force=True)
center, width = window_values(sample_ds, window)
if plane == "coronal":
index = min(index, stack.shape[1] - 1)
arr = stack[:, index, :]
spacing = (stack_data["slice_spacing"], stack_data["col_spacing"])
elif plane == "sagittal":
index = min(index, stack.shape[2] - 1)
arr = stack[:, :, index]
spacing = (stack_data["slice_spacing"], stack_data["row_spacing"])
else:
raise HTTPException(status_code=400, detail="invalid plane")
payload = render_array(np.flipud(arr), center, width, False, rotate)
payload = render_array(np.flipud(arr), center, width, False, rotate, pixel_spacing=spacing)
return Response(payload, media_type="image/png")
@@ -644,22 +945,45 @@ def dicom_info(ct_number: str, series_uid: str, index: int = 0, _: str = Depends
return {"path": str(path), "fields": fields}
@app.put("/api/series/{ct_number}/{series_uid}/annotation")
def save_annotation(ct_number: str, series_uid: str, data: AnnotationIn, user: str = Depends(require_auth)) -> dict[str, Any]:
body_parts = [] if data.skipped else [part for part in data.body_parts if part in BODY_PARTS]
phase = data.upper_abdomen_phase if data.upper_abdomen_phase in PHASES else ""
if "upper_abdomen" not in body_parts:
phase = ""
study = scan_study(ct_number)
series_row = next((row for row in study["series"] if row["series_uid"] == series_uid), None)
if not series_row:
raise HTTPException(status_code=404, detail="series not found")
def sql_bool_or_null(value: bool | None) -> str:
if value is None:
return "NULL"
return "true" if value else "false"
def save_annotation_payload(
ct_number: str,
series_uid: str,
series_row: dict[str, Any],
manual_parts: list[str],
ai_parts: list[str],
manual_phase: str,
ai_phase: str,
manual_plain_ct: bool | None,
ai_plain_ct: bool | None,
skipped: bool,
ai_skipped: bool,
notes: str,
user: str,
ai_result: dict[str, Any] | None = None,
ai_model: str | None = None,
) -> dict[str, Any]:
manual_parts = valid_parts(manual_parts)
ai_parts = valid_parts(ai_parts)
body_parts = merge_parts(manual_parts, ai_parts, skipped)
manual_phase = valid_phase(manual_phase)
ai_phase = valid_phase(ai_phase)
phase = effective_phase(manual_phase, ai_phase, body_parts, skipped)
plain_ct = effective_plain_ct(manual_plain_ct, ai_plain_ct, False, skipped)
ensure_annotation_table()
pg_scalar(
f"""
INSERT INTO public.pacs_dicom_series_annotations (
ct_number, study_instance_uid, series_instance_uid, series_description,
body_parts, upper_abdomen_phase, skipped, notes, updated_by, updated_at
body_parts, manual_body_parts, ai_body_parts,
upper_abdomen_phase, manual_upper_abdomen_phase, ai_upper_abdomen_phase,
plain_ct, manual_plain_ct, ai_plain_ct,
skipped, ai_skipped, notes, ai_result, ai_model, updated_by, updated_at
)
VALUES (
{sql_literal(ct_number)},
@@ -667,9 +991,19 @@ def save_annotation(ct_number: str, series_uid: str, data: AnnotationIn, user: s
{sql_literal(series_uid)},
{sql_literal(series_row.get('description', ''))},
{sql_literal(json.dumps(body_parts, ensure_ascii=False))}::jsonb,
{sql_literal(json.dumps([] if skipped else manual_parts, ensure_ascii=False))}::jsonb,
{sql_literal(json.dumps([] if skipped else ai_parts, ensure_ascii=False))}::jsonb,
{sql_literal(phase)},
{'true' if data.skipped else 'false'},
{sql_literal(data.notes)},
{sql_literal('' if skipped else manual_phase)},
{sql_literal('' if skipped else ai_phase)},
{'true' if plain_ct else 'false'},
{sql_bool_or_null(None if skipped else manual_plain_ct)},
{sql_bool_or_null(None if skipped else ai_plain_ct)},
{'true' if skipped else 'false'},
{'true' if ai_skipped else 'false'},
{sql_literal(notes)},
{sql_literal(json.dumps(ai_result, ensure_ascii=False)) + '::jsonb' if ai_result is not None else 'NULL'},
{sql_literal(ai_model) if ai_model is not None else 'NULL'},
{sql_literal(user)},
now()
)
@@ -677,15 +1011,65 @@ def save_annotation(ct_number: str, series_uid: str, data: AnnotationIn, user: s
study_instance_uid = EXCLUDED.study_instance_uid,
series_description = EXCLUDED.series_description,
body_parts = EXCLUDED.body_parts,
manual_body_parts = EXCLUDED.manual_body_parts,
ai_body_parts = EXCLUDED.ai_body_parts,
upper_abdomen_phase = EXCLUDED.upper_abdomen_phase,
manual_upper_abdomen_phase = EXCLUDED.manual_upper_abdomen_phase,
ai_upper_abdomen_phase = EXCLUDED.ai_upper_abdomen_phase,
plain_ct = EXCLUDED.plain_ct,
manual_plain_ct = EXCLUDED.manual_plain_ct,
ai_plain_ct = EXCLUDED.ai_plain_ct,
skipped = EXCLUDED.skipped,
ai_skipped = EXCLUDED.ai_skipped,
notes = EXCLUDED.notes,
ai_result = COALESCE(EXCLUDED.ai_result, pacs_dicom_series_annotations.ai_result),
ai_model = COALESCE(EXCLUDED.ai_model, pacs_dicom_series_annotations.ai_model),
updated_by = EXCLUDED.updated_by,
updated_at = now()
"""
)
return normalize_annotation(
{
"body_parts": body_parts,
"manual_body_parts": [] if skipped else manual_parts,
"ai_body_parts": [] if skipped else ai_parts,
"upper_abdomen_phase": phase,
"manual_upper_abdomen_phase": "" if skipped else manual_phase,
"ai_upper_abdomen_phase": "" if skipped else ai_phase,
"plain_ct": plain_ct,
"manual_plain_ct": None if skipped else manual_plain_ct,
"ai_plain_ct": None if skipped else ai_plain_ct,
"skipped": skipped,
"ai_skipped": ai_skipped,
"notes": notes,
"ai_model": ai_model or series_row.get("annotation", {}).get("ai_model", ""),
}
)
@app.put("/api/series/{ct_number}/{series_uid}/annotation")
def save_annotation(ct_number: str, series_uid: str, data: AnnotationIn, user: str = Depends(require_auth)) -> dict[str, Any]:
study = scan_study(ct_number)
series_row = next((row for row in study["series"] if row["series_uid"] == series_uid), None)
if not series_row:
raise HTTPException(status_code=404, detail="series not found")
annotation = save_annotation_payload(
ct_number=ct_number,
series_uid=series_uid,
series_row=series_row,
manual_parts=data.manual_body_parts or data.body_parts,
ai_parts=data.ai_body_parts,
manual_phase=data.manual_upper_abdomen_phase or data.upper_abdomen_phase,
ai_phase=data.ai_upper_abdomen_phase,
manual_plain_ct=data.manual_plain_ct if data.manual_plain_ct is not None else (data.plain_ct if data.plain_ct and data.ai_plain_ct is None else None),
ai_plain_ct=data.ai_plain_ct,
skipped=data.skipped,
ai_skipped=data.ai_skipped,
notes=data.notes,
user=user,
)
STUDY_CACHE.pop(ct_number, None)
return {"ok": True, "body_parts": body_parts, "upper_abdomen_phase": phase, "skipped": data.skipped}
return {"ok": True, **annotation}
def representative_images(ct_number: str, series_uid: str) -> list[tuple[str, bytes]]:
@@ -693,17 +1077,30 @@ def representative_images(ct_number: str, series_uid: str) -> list[tuple[str, by
axial_index = max(0, min(len(files) - 1, len(files) // 2))
ds = pydicom.dcmread(str(files[axial_index]), force=True)
center, width = window_values(ds, "soft")
images = [("横断面", render_array(dicom_to_hu(ds), center, width, getattr(ds, "PhotometricInterpretation", "") == "MONOCHROME1", max_size=720))]
images = [
(
"轴位原始",
render_array(
dicom_to_hu(ds),
center,
width,
getattr(ds, "PhotometricInterpretation", "") == "MONOCHROME1",
max_size=720,
pixel_spacing=pixel_spacing_from_ds(ds),
),
)
]
if len(files) >= 3:
try:
stack = load_stack(ct_number, series_uid)
stack_data = load_stack_data(ct_number, series_uid)
stack = stack_data["stack"]
sample_ds = pydicom.dcmread(str(files[axial_index]), stop_before_pixels=True, force=True)
center, width = window_values(sample_ds, "soft")
coronal = np.flipud(stack[:, stack.shape[1] // 2, :])
sagittal = np.flipud(stack[:, :, stack.shape[2] // 2])
images.append(("矢状面", render_array(sagittal, center, width, False, max_size=720)))
images.append(("冠状面", render_array(coronal, center, width, False, max_size=720)))
images.append(("矢状位重建", render_array(sagittal, center, width, False, max_size=720, pixel_spacing=(stack_data["slice_spacing"], stack_data["row_spacing"]))))
images.append(("冠状位重建", render_array(coronal, center, width, False, max_size=720, pixel_spacing=(stack_data["slice_spacing"], stack_data["col_spacing"]))))
except Exception:
pass
return images
@@ -742,8 +1139,9 @@ def ai_classify(ct_number: str, series_uid: str, _: AIRequest, user: str = Depen
"可选部位键: head_neck(头颈部), chest(胸部), upper_abdomen(上腹部), "
"lower_abdomen(下腹部), pelvis(盆腔)。一个序列可包含多个部位。"
"如果不是可用于标注的平扫CT影像、定位像、剂量报告或无法判断，请 skipped=true。"
"请同时判断 plain_ct 是否为平扫CT。"
"如果包含上腹部，请判断期相: arterial(动脉期)、portal_venous(门静脉期)、unknown(无法判别)。"
"只返回JSON: {\"body_parts\":[],\"upper_abdomen_phase\":\"\",\"skipped\":false,\"notes\":\"\"}。"
"只返回JSON: {\"body_parts\":[],\"upper_abdomen_phase\":\"\",\"plain_ct\":false,\"skipped\":false,\"notes\":\"\"}。"
f"PACS张数: {series_row.get('count', 0)}。"
f"序列描述: {series_row.get('description','')}；DICOM部位: {series_row.get('body_part_dicom','')}。"
),
@@ -781,36 +1179,27 @@ def ai_classify(ct_number: str, series_uid: str, _: AIRequest, user: str = Depen
suggestion = parse_ai_json(message)
body_parts = [part for part in suggestion.get("body_parts", []) if part in BODY_PARTS]
skipped = bool(suggestion.get("skipped", False))
plain_ct = bool(suggestion.get("plain_ct", False))
phase = suggestion.get("upper_abdomen_phase", "")
if phase not in PHASES or "upper_abdomen" not in body_parts:
phase = ""
ensure_annotation_table()
pg_scalar(
f"""
INSERT INTO public.pacs_dicom_series_annotations (
ct_number, study_instance_uid, series_instance_uid, series_description,
body_parts, upper_abdomen_phase, skipped, notes, ai_result, ai_model, updated_by, updated_at
)
VALUES (
{sql_literal(ct_number)},
{sql_literal(series_row.get('study_uid', ''))},
{sql_literal(series_uid)},
{sql_literal(series_row.get('description', ''))},
'[]'::jsonb,
'',
false,
'',
{sql_literal(json.dumps(suggestion, ensure_ascii=False))}::jsonb,
{sql_literal(KIMI_MODEL)},
{sql_literal(user)},
now()
)
ON CONFLICT (ct_number, series_instance_uid) DO UPDATE SET
ai_result = EXCLUDED.ai_result,
ai_model = EXCLUDED.ai_model,
updated_by = EXCLUDED.updated_by,
updated_at = now()
"""
current = normalize_annotation(series_row.get("annotation", {}))
annotation = save_annotation_payload(
ct_number=ct_number,
series_uid=series_uid,
series_row=series_row,
manual_parts=current.get("manual_body_parts", []),
ai_parts=[] if skipped else body_parts,
manual_phase=current.get("manual_upper_abdomen_phase", ""),
ai_phase="" if skipped else phase,
manual_plain_ct=current.get("manual_plain_ct"),
ai_plain_ct=None if skipped else plain_ct,
skipped=skipped,
ai_skipped=skipped,
notes=str(suggestion.get("notes", "")) or current.get("notes", ""),
user=user,
ai_result=suggestion,
ai_model=KIMI_MODEL,
)
STUDY_CACHE.pop(ct_number, None)
return {
@@ -818,10 +1207,7 @@ def ai_classify(ct_number: str, series_uid: str, _: AIRequest, user: str = Depen
"provider": "Kimi",
"name": KIMI_API_NAME,
"model": KIMI_MODEL,
"body_parts": [] if skipped else body_parts,
"upper_abdomen_phase": "" if skipped else phase,
"skipped": skipped,
"notes": str(suggestion.get("notes", "")),
**annotation,
"raw": suggestion,
}

389
PACS_DICOM处理/数据处理网页端/static/app.js
View File

@@ -4,6 +4,7 @@ const app = {
study: null,
series: [],
activeSeries: null,
draft: null,
slice: 0,
plane: "axial",
window: "default",
@@ -14,12 +15,21 @@ const app = {
imageUrl: "",
searchTimer: null,
statusTimer: null,
saveTimer: null,
pendingImage: null,
drag: null,
seriesSort: "asc",
showOverlay: true,
};
const $ = (id) => document.getElementById(id);
const clamp = (value, min, max) => Math.max(min, Math.min(max, value));
const BODY_PARTS = ["head_neck", "chest", "upper_abdomen", "lower_abdomen", "pelvis"];
const WINDOW_PRESETS = {
bone: { wl: 500, ww: 1800 },
soft: { wl: 50, ww: 360 },
contrast: { wl: 90, ww: 140 },
};
function escapeHtml(value) {
return String(value ?? "")
@@ -80,6 +90,10 @@ function timeRange(series) {
return first || last || "未记录";
}
function timeKey(series) {
return String(series.series_time || series.first_time || series.study_time || "").replace(/\D/g, "").padEnd(6, "0");
}
function phaseLabel(value) {
return { arterial: "动脉期", portal_venous: "门静脉期", unknown: "无法判别" }[value] || "";
}
@@ -94,6 +108,28 @@ function partLabel(value) {
}[value] || value;
}
function asList(value) {
return Array.isArray(value) ? value : [];
}
function uniq(list) {
return Array.from(new Set(list.filter(Boolean)));
}
function parseFirstNumber(value) {
const match = String(value || "").match(/-?\d+(\.\d+)?/);
return match ? Number(match[0]) : null;
}
function ageText(birthDate, studyDate) {
const birth = String(birthDate || "");
const study = String(studyDate || "");
if (birth.length !== 8 || study.length !== 8) return "";
let age = Number(study.slice(0, 4)) - Number(birth.slice(0, 4));
if (study.slice(4) < birth.slice(4)) age -= 1;
return age > 0 ? `${age}Y` : "";
}
async function login(event) {
event.preventDefault();
$("loginError").textContent = "";
@@ -132,6 +168,22 @@ async function refreshStatus() {
}
}
function sortSeries(list) {
return [...list].sort((a, b) => {
const skipA = a.annotation?.skipped ? 1 : 0;
const skipB = b.annotation?.skipped ? 1 : 0;
if (skipA !== skipB) return skipA - skipB;
const result = timeKey(a).localeCompare(timeKey(b)) || String(a.series_number || "").localeCompare(String(b.series_number || ""), undefined, { numeric: true });
return app.seriesSort === "asc" ? result : -result;
});
}
function setSeries(list) {
app.series = sortSeries(list || []);
$("seriesCount").textContent = String(app.series.length);
renderSeries();
}
async function loadStudies() {
const q = encodeURIComponent($("studySearch").value.trim());
app.studies = await json(`/api/studies?q=${q}&limit=500`);
@@ -163,6 +215,7 @@ async function selectStudy(ctNumber) {
app.study = app.studies.find((item) => item.ct_number === ctNumber) || { ct_number: ctNumber };
app.series = [];
app.activeSeries = null;
app.draft = null;
$("activeStudyLabel").textContent = ctNumber;
$("seriesCount").textContent = "读取中";
$("seriesGrid").innerHTML = "";
@@ -171,24 +224,45 @@ async function selectStudy(ctNumber) {
try {
const data = await json(`/api/studies/${encodeURIComponent(ctNumber)}/series`);
app.study = data.study;
app.series = data.series || [];
$("seriesCount").textContent = String(app.series.length);
renderSeries();
setSeries(data.series || []);
if (app.series.length) selectSeries(app.series[0].series_uid);
} catch (err) {
$("seriesGrid").innerHTML = `<p class="error-line">${escapeHtml(err.message)}</p>`;
}
}
function sourceTag(value, annotation) {
if (asList(annotation.manual_body_parts).includes(value)) return "manual";
if (asList(annotation.ai_body_parts).includes(value)) return "ai";
return "";
}
function seriesTags(series) {
const annotation = series.annotation || {};
if (annotation.skipped) {
return [{ label: "略过", source: annotation.ai_skipped ? "ai" : "manual" }];
}
const tags = [];
if (annotation.plain_ct) {
tags.push({ label: "平扫CT", source: annotation.manual_plain_ct !== null && annotation.manual_plain_ct !== undefined ? "manual" : "ai" });
}
for (const part of asList(annotation.body_parts)) {
tags.push({ label: partLabel(part), source: sourceTag(part, annotation) });
}
const phase = phaseLabel(annotation.upper_abdomen_phase);
if (phase) {
tags.push({ label: phase, source: annotation.manual_upper_abdomen_phase ? "manual" : "ai" });
}
if (series.body_part_dicom) tags.unshift({ label: series.body_part_dicom, source: "" });
return tags;
}
function renderSeries() {
const grid = $("seriesGrid");
grid.innerHTML = "";
for (const series of app.series) {
const skipped = Boolean(series.annotation?.skipped);
const parts = series.annotation?.body_parts || [];
const tags = skipped
? ["略过"]
: [series.body_part_dicom, ...parts.map(partLabel), phaseLabel(series.annotation?.upper_abdomen_phase)].filter(Boolean);
const tags = seriesTags(series);
const card = document.createElement("button");
card.className = "series-card";
card.classList.toggle("active", app.activeSeries?.series_uid === series.series_uid);
@@ -203,7 +277,7 @@ function renderSeries() {
<strong>${escapeHtml(series.description || "未命名序列")}</strong>
<span class="shot-time">拍摄 ${escapeHtml(timeRange(series))}</span>
<small>序列 ${escapeHtml(series.series_number || "-")} · ${escapeHtml(series.rows || "-")}×${escapeHtml(series.columns || "-")} · ${escapeHtml(series.modality || "")}</small>
<div class="tag-line">${tags.length ? tags.map((tag) => `<em>${escapeHtml(tag)}</em>`).join("") : "<em>未标注</em>"}</div>
<div class="tag-line">${tags.length ? tags.map((tag) => `<em class="${tag.source ? `tag-${tag.source}` : ""}">${escapeHtml(tag.label)}</em>`).join("") : "<em>未标注</em>"}</div>
</div>
`;
card.onclick = () => selectSeries(series.series_uid);
@@ -262,12 +336,56 @@ function resetViewer() {
$("dicomImage").removeAttribute("src");
$("imageEmpty").classList.remove("hidden");
$("sliceText").textContent = "0 / 0";
$("saveState").textContent = "未保存";
$("saveState").textContent = "自动保存";
resetViewState();
updateOverlay();
}
function applyTransform() {
$("dicomImage").style.transform = `translate(${app.panX}px, ${app.panY}px) scale(${app.zoom}) rotate(${app.rotate}deg)`;
updateOverlay();
}
function windowInfo() {
if (WINDOW_PRESETS[app.window]) return WINDOW_PRESETS[app.window];
return {
wl: parseFirstNumber(app.activeSeries?.window_center) ?? 50,
ww: parseFirstNumber(app.activeSeries?.window_width) ?? 360,
};
}
function orientationLabels() {
if (app.plane === "sagittal") return { top: "H", bottom: "F", left: "A", right: "P" };
if (app.plane === "coronal") return { top: "H", bottom: "F", left: "R", right: "L" };
return { top: "A", bottom: "P", left: "R", right: "L" };
}
function updateOverlay() {
const overlay = $("imageOverlay");
overlay.classList.toggle("hidden", !app.showOverlay || !app.activeSeries);
$("overlayToggle").textContent = app.showOverlay ? "隐藏信息" : "显示信息";
if (!app.activeSeries) return;
const s = app.activeSeries;
const orient = orientationLabels();
const win = windowInfo();
const age = ageText(s.patient_birth_date, s.study_date || app.study?.study_date);
const sex = s.patient_sex || "";
document.querySelector(".ov-left-top").innerHTML = [
s.patient_name || app.study?.source_patient_name || "",
[fmtDate(s.patient_birth_date), age, sex].filter(Boolean).join(" "),
s.patient_id || app.study?.patient_id || "",
fmtDate(s.study_date || app.study?.study_date),
].filter(Boolean).map(escapeHtml).join("<br />");
document.querySelector(".ov-right-top").innerHTML = [
s.institution_name || "",
s.manufacturer || "",
].filter(Boolean).map(escapeHtml).join("<br />");
document.querySelector(".ov-left-mid").textContent = orient.left;
document.querySelector(".ov-right-mid").textContent = orient.right;
document.querySelector(".ov-top-mid").textContent = orient.top;
document.querySelector(".ov-bottom-mid").textContent = orient.bottom;
document.querySelector(".ov-left-bottom").textContent = `Img:${app.slice + 1}/${maxSlice() + 1}`;
document.querySelector(".ov-right-bottom").innerHTML = `WW:${Math.round(win.ww)}<br />WL:${Math.round(win.wl)}<br />Zoom:${app.zoom.toFixed(2)}`;
}
async function updateImage() {
@@ -278,6 +396,7 @@ async function updateImage() {
$("sliceSlider").value = String(app.slice);
$("sliceText").textContent = `${app.slice + 1} / ${max + 1}`;
$("imageEmpty").classList.add("hidden");
updateOverlay();
const ticket = Symbol("image");
app.pendingImage = ticket;
try {
@@ -292,95 +411,186 @@ async function updateImage() {
}
}
function isSkippedChecked() {
return Boolean(document.querySelector('.part-grid input[value="skip"]')?.checked);
function cloneAnnotation(annotation = {}) {
return {
body_parts: asList(annotation.body_parts),
manual_body_parts: asList(annotation.manual_body_parts),
ai_body_parts: asList(annotation.ai_body_parts),
upper_abdomen_phase: annotation.upper_abdomen_phase || "",
manual_upper_abdomen_phase: annotation.manual_upper_abdomen_phase || "",
ai_upper_abdomen_phase: annotation.ai_upper_abdomen_phase || "",
plain_ct: Boolean(annotation.plain_ct),
manual_plain_ct: annotation.manual_plain_ct ?? null,
ai_plain_ct: annotation.ai_plain_ct ?? null,
skipped: Boolean(annotation.skipped),
ai_skipped: Boolean(annotation.ai_skipped),
notes: annotation.notes || "",
ai_model: annotation.ai_model || "",
};
}
function selectedParts() {
return Array.from(document.querySelectorAll('.part-grid input:checked:not([value="skip"])')).map((input) => input.value);
function effectiveParts() {
if (!app.draft || app.draft.skipped) return [];
return uniq([...app.draft.manual_body_parts, ...app.draft.ai_body_parts]);
}
function syncPartState() {
const skipped = isSkippedChecked();
document.querySelectorAll('.part-grid input:not([value="skip"])').forEach((input) => {
input.disabled = skipped;
if (skipped) input.checked = false;
function effectivePhase() {
if (!effectiveParts().includes("upper_abdomen")) return "";
return app.draft.manual_upper_abdomen_phase || app.draft.ai_upper_abdomen_phase || "";
}
function effectivePlainCt() {
if (!app.draft || app.draft.skipped) return false;
if (app.draft.manual_plain_ct !== null && app.draft.manual_plain_ct !== undefined) return Boolean(app.draft.manual_plain_ct);
if (app.draft.ai_plain_ct !== null && app.draft.ai_plain_ct !== undefined) return Boolean(app.draft.ai_plain_ct);
return Boolean(app.draft.plain_ct);
}
function setLabelSource(input, source) {
const label = input.closest("label");
label.classList.toggle("manual-selected", source === "manual");
label.classList.toggle("ai-selected", source === "ai");
}
function applyAnnotationControls() {
if (!app.draft) return;
const parts = new Set(effectiveParts());
document.querySelectorAll(".part-grid input").forEach((input) => {
const value = input.value;
if (value === "skip") {
input.checked = app.draft.skipped;
input.disabled = false;
setLabelSource(input, app.draft.skipped ? (app.draft.ai_skipped ? "ai" : "manual") : "");
return;
}
if (value === "plain_ct") {
input.checked = effectivePlainCt();
input.disabled = app.draft.skipped;
setLabelSource(input, app.draft.manual_plain_ct !== null && app.draft.manual_plain_ct !== undefined ? "manual" : app.draft.ai_plain_ct !== null && app.draft.ai_plain_ct !== undefined ? "ai" : "");
return;
}
input.checked = parts.has(value);
input.disabled = app.draft.skipped;
setLabelSource(input, app.draft.manual_body_parts.includes(value) ? "manual" : app.draft.ai_body_parts.includes(value) ? "ai" : "");
});
if (skipped) {
document.querySelectorAll("input[name=phase]").forEach((input) => {
input.checked = false;
});
}
const show = !skipped && selectedParts().includes("upper_abdomen");
$("phaseBox").classList.toggle("visible", show);
const phase = effectivePhase();
document.querySelectorAll("input[name=phase]").forEach((input) => {
input.checked = input.value === phase;
input.disabled = app.draft.skipped;
setLabelSource(input, app.draft.manual_upper_abdomen_phase === input.value ? "manual" : app.draft.ai_upper_abdomen_phase === input.value ? "ai" : "");
});
$("phaseBox").classList.toggle("visible", !app.draft.skipped && parts.has("upper_abdomen"));
}
function hydrateAnnotation() {
const annotation = app.activeSeries?.annotation || {};
const parts = new Set(annotation.body_parts || []);
document.querySelectorAll(".part-grid input").forEach((input) => {
if (input.value === "skip") {
input.checked = Boolean(annotation.skipped);
return;
}
input.checked = !annotation.skipped && parts.has(input.value);
input.disabled = Boolean(annotation.skipped);
});
document.querySelectorAll("input[name=phase]").forEach((input) => {
input.checked = !annotation.skipped && input.value === (annotation.upper_abdomen_phase || "");
});
$("annotationNotes").value = annotation.notes || "";
syncPartState();
app.draft = cloneAnnotation(app.activeSeries?.annotation || {});
$("annotationNotes").value = app.draft.notes || "";
applyAnnotationControls();
}
async function reloadCurrentStudySeries(keepUid) {
const data = await json(`/api/studies/${encodeURIComponent(app.study.ct_number)}/series`);
app.study = data.study;
app.series = data.series || [];
app.activeSeries = app.series.find((item) => item.series_uid === keepUid) || app.series[0] || null;
$("seriesCount").textContent = String(app.series.length);
function updateLocalAnnotation(annotation) {
const normalized = cloneAnnotation(annotation);
normalized.body_parts = asList(annotation.body_parts);
normalized.upper_abdomen_phase = annotation.upper_abdomen_phase || "";
app.draft = normalized;
app.activeSeries.annotation = normalized;
const index = app.series.findIndex((item) => item.series_uid === app.activeSeries.series_uid);
if (index >= 0) app.series[index].annotation = normalized;
app.series = sortSeries(app.series);
renderSeries();
hydrateAnnotation();
applyAnnotationControls();
}
async function saveAnnotation() {
if (!app.study || !app.activeSeries) return;
const skipped = isSkippedChecked();
const parts = skipped ? [] : selectedParts();
let phase = skipped ? "" : document.querySelector("input[name=phase]:checked")?.value || "";
if (parts.includes("upper_abdomen") && !phase) {
$("saveState").textContent = "请选择上腹部期相";
return;
}
if (!parts.includes("upper_abdomen")) phase = "";
function annotationPayload() {
const bodyParts = app.draft.skipped ? [] : effectiveParts();
return {
body_parts: bodyParts,
manual_body_parts: app.draft.skipped ? [] : app.draft.manual_body_parts,
ai_body_parts: app.draft.skipped ? [] : app.draft.ai_body_parts,
upper_abdomen_phase: app.draft.skipped ? "" : effectivePhase(),
manual_upper_abdomen_phase: app.draft.skipped ? "" : app.draft.manual_upper_abdomen_phase,
ai_upper_abdomen_phase: app.draft.skipped ? "" : app.draft.ai_upper_abdomen_phase,
plain_ct: effectivePlainCt(),
manual_plain_ct: app.draft.skipped ? null : app.draft.manual_plain_ct,
ai_plain_ct: app.draft.skipped ? null : app.draft.ai_plain_ct,
skipped: app.draft.skipped,
ai_skipped: app.draft.ai_skipped,
notes: $("annotationNotes").value,
};
}
async function saveAnnotationNow() {
if (!app.study || !app.activeSeries || !app.draft) return;
$("saveState").textContent = "保存中";
const uid = app.activeSeries.series_uid;
try {
await json(`/api/series/${encodeURIComponent(app.study.ct_number)}/${encodeURIComponent(uid)}/annotation`, {
const data = await json(`/api/series/${encodeURIComponent(app.study.ct_number)}/${encodeURIComponent(uid)}/annotation`, {
method: "PUT",
body: JSON.stringify({ body_parts: parts, upper_abdomen_phase: phase, skipped, notes: $("annotationNotes").value }),
body: JSON.stringify(annotationPayload()),
});
$("saveState").textContent = "已保存";
await reloadCurrentStudySeries(uid);
updateLocalAnnotation(data);
} catch (err) {
$("saveState").textContent = err.message;
}
}
function applyAiSuggestion(data) {
const parts = new Set(data.body_parts || []);
document.querySelectorAll(".part-grid input").forEach((input) => {
if (input.value === "skip") {
input.checked = Boolean(data.skipped);
return;
function queueSave(delay = 450) {
clearTimeout(app.saveTimer);
$("saveState").textContent = "待保存";
app.saveTimer = setTimeout(saveAnnotationNow, delay);
}
function handlePartChange(event) {
if (!app.draft) return;
const input = event.target;
const value = input.value;
if (value === "skip") {
app.draft.skipped = input.checked;
if (!input.checked) app.draft.ai_skipped = false;
if (input.checked) {
app.draft.manual_body_parts = [];
app.draft.ai_body_parts = [];
app.draft.manual_upper_abdomen_phase = "";
app.draft.ai_upper_abdomen_phase = "";
app.draft.manual_plain_ct = null;
app.draft.ai_plain_ct = null;
}
input.checked = !data.skipped && parts.has(input.value);
});
document.querySelectorAll("input[name=phase]").forEach((input) => {
input.checked = !data.skipped && input.value === (data.upper_abdomen_phase || "");
});
if (data.notes) $("annotationNotes").value = data.notes;
syncPartState();
} else if (value === "plain_ct") {
if (input.checked) {
app.draft.manual_plain_ct = true;
app.draft.ai_plain_ct = null;
} else {
app.draft.manual_plain_ct = false;
app.draft.ai_plain_ct = null;
}
} else if (BODY_PARTS.includes(value)) {
if (input.checked) {
app.draft.manual_body_parts = uniq([...app.draft.manual_body_parts, value]);
} else {
app.draft.manual_body_parts = app.draft.manual_body_parts.filter((part) => part !== value);
app.draft.ai_body_parts = app.draft.ai_body_parts.filter((part) => part !== value);
if (value === "upper_abdomen") {
app.draft.manual_upper_abdomen_phase = "";
app.draft.ai_upper_abdomen_phase = "";
}
}
}
app.draft.body_parts = effectiveParts();
app.draft.upper_abdomen_phase = effectivePhase();
app.draft.plain_ct = effectivePlainCt();
applyAnnotationControls();
queueSave();
}
function handlePhaseChange(event) {
if (!app.draft) return;
app.draft.manual_upper_abdomen_phase = event.target.value;
if (app.draft.ai_upper_abdomen_phase !== event.target.value) app.draft.ai_upper_abdomen_phase = "";
app.draft.upper_abdomen_phase = effectivePhase();
applyAnnotationControls();
queueSave();
}
async function runAI() {
@@ -393,8 +603,9 @@ async function runAI() {
method: "POST",
body: JSON.stringify({ sample_count: 3 }),
});
applyAiSuggestion(data);
$("saveState").textContent = "AI 建议已填入，确认后保存";
updateLocalAnnotation(data);
$("annotationNotes").value = data.notes || "";
$("saveState").textContent = "AI 结果已保存";
} catch (err) {
$("saveState").textContent = err.message;
} finally {
@@ -530,7 +741,7 @@ function wireImageGestures() {
{ passive: false },
);
wrap.addEventListener("pointerdown", (event) => {
if (!app.activeSeries) return;
if (!app.activeSeries || event.target.closest(".slice-rail")) return;
app.drag = { x: event.clientX, y: event.clientY, panX: app.panX, panY: app.panY };
wrap.classList.add("dragging");
wrap.setPointerCapture(event.pointerId);
@@ -549,6 +760,17 @@ function wireImageGestures() {
});
}
function setSort(direction) {
app.seriesSort = direction;
$("sortAsc").classList.toggle("active", direction === "asc");
$("sortDesc").classList.toggle("active", direction === "desc");
app.series = sortSeries(app.series);
renderSeries();
if (app.activeSeries) {
app.activeSeries = app.series.find((item) => item.series_uid === app.activeSeries.series_uid) || app.activeSeries;
}
}
function wire() {
$("loginForm").addEventListener("submit", login);
$("logoutBtn").addEventListener("click", logout);
@@ -557,6 +779,12 @@ function wire() {
$("aiClassify").addEventListener("click", runAI);
$("zoomIn").addEventListener("click", () => changeZoom(1.18));
$("zoomOut").addEventListener("click", () => changeZoom(1 / 1.18));
$("overlayToggle").addEventListener("click", () => {
app.showOverlay = !app.showOverlay;
updateOverlay();
});
$("sortAsc").addEventListener("click", () => setSort("asc"));
$("sortDesc").addEventListener("click", () => setSort("desc"));
$("studySearch").addEventListener("input", () => {
clearTimeout(app.searchTimer);
app.searchTimer = setTimeout(loadStudies, 250);
@@ -589,8 +817,13 @@ function wire() {
applyTransform();
});
$("resetView").addEventListener("click", resetViewState);
document.querySelectorAll(".part-grid input").forEach((input) => input.addEventListener("change", syncPartState));
$("saveAnnotation").addEventListener("click", saveAnnotation);
document.querySelectorAll(".part-grid input").forEach((input) => input.addEventListener("change", handlePartChange));
document.querySelectorAll("input[name=phase]").forEach((input) => input.addEventListener("change", handlePhaseChange));
$("annotationNotes").addEventListener("input", () => {
if (!app.draft) return;
app.draft.notes = $("annotationNotes").value;
queueSave(900);
});
document.querySelectorAll("[data-close]").forEach((button) => {
button.addEventListener("click", () => $(button.dataset.close).classList.add("hidden"));
});

33
PACS_DICOM处理/数据处理网页端/static/index.html
View File

@@ -53,7 +53,11 @@
<section class="series-pane">
<div class="pane-head">
<h2>序列</h2>
<span id="seriesCount">0</span>
<div class="series-head-tools">
<button id="sortAsc" class="sort-btn active">时间 ↑</button>
<button id="sortDesc" class="sort-btn">时间 ↓</button>
<span id="seriesCount">0</span>
</div>
</div>
<div id="seriesGrid" class="series-grid"></div>
</section>
@@ -61,9 +65,9 @@
<section class="viewer-pane">
<div class="viewer-toolbar">
<div class="segmented" data-group="plane">
<button data-plane="axial" class="active">横断面</button>
<button data-plane="sagittal">矢状面</button>
<button data-plane="coronal">冠状面</button>
<button data-plane="axial" class="active">轴位原始</button>
<button data-plane="sagittal">矢状位重建</button>
<button data-plane="coronal">冠状位重建</button>
</div>
<div class="segmented" data-group="window">
<button data-window="default" class="active">默认</button>
@@ -77,6 +81,7 @@
<button id="zoomOut">− 缩小</button>
<button id="zoomIn">+ 放大</button>
<button id="resetView">↺ 复位</button>
<button id="overlayToggle">隐藏信息</button>
<button id="infoBtn">DICOM 信息</button>
</div>
</div>
@@ -84,6 +89,18 @@
<div class="viewer-stage">
<div class="image-wrap">
<img id="dicomImage" alt="" />
<div id="imageOverlay" class="image-overlay">
<div class="crosshair crosshair-v"></div>
<div class="crosshair crosshair-h"></div>
<div class="ov ov-left-top"></div>
<div class="ov ov-right-top"></div>
<div class="ov ov-left-mid"></div>
<div class="ov ov-right-mid"></div>
<div class="ov ov-top-mid"></div>
<div class="ov ov-bottom-mid"></div>
<div class="ov ov-left-bottom"></div>
<div class="ov ov-right-bottom"></div>
</div>
<div id="imageEmpty" class="image-empty"></div>
<div class="slice-rail">
<input id="sliceSlider" type="range" min="0" max="0" value="0" orient="vertical" />
@@ -95,12 +112,17 @@
<div class="annotation-head">
<div>
<h2>部位标注</h2>
<span id="saveState">未保存</span>
<span id="saveState">自动保存</span>
</div>
<button id="aiClassify" class="ghost-btn ai-btn">AI 识别</button>
</div>
<div class="source-legend">
<span><i class="manual-dot"></i>人工</span>
<span><i class="ai-dot"></i>AI</span>
</div>
<div class="part-grid">
<label class="skip-option"><input type="checkbox" value="skip" />略过</label>
<label class="plain-option"><input type="checkbox" value="plain_ct" />平扫CT</label>
<label><input type="checkbox" value="head_neck" />头颈部</label>
<label><input type="checkbox" value="chest" />胸部</label>
<label><input type="checkbox" value="upper_abdomen" />上腹部</label>
@@ -117,7 +139,6 @@
</div>
<div class="annotation-actions">
<input id="annotationNotes" class="note-input" placeholder="备注" />
<button id="saveAnnotation" class="primary-btn">保存标注</button>
</div>
</aside>
</div>

176
PACS_DICOM处理/数据处理网页端/static/styles.css
View File

@@ -284,6 +284,28 @@ button:disabled {
font-size: 12px;
}
.series-head-tools {
display: flex;
align-items: center;
gap: 6px;
}
.sort-btn {
height: 26px;
padding: 0 8px;
border: 1px solid var(--stroke);
border-radius: 7px;
background: #0b0f16;
color: var(--muted);
font-size: 12px;
}
.sort-btn.active {
border-color: var(--blue);
background: rgba(52, 116, 246, 0.2);
color: var(--text);
}
.study-list,
.series-grid {
min-height: 0;
@@ -433,6 +455,18 @@ button:disabled {
white-space: nowrap;
}
.tag-line .tag-manual {
border-color: rgba(25, 212, 194, 0.58);
color: #baf8ee;
background: rgba(25, 212, 194, 0.08);
}
.tag-line .tag-ai {
border-color: rgba(240, 181, 78, 0.65);
color: #ffe0a3;
background: rgba(240, 181, 78, 0.1);
}
.viewer-pane {
min-width: 0;
display: grid;
@@ -502,6 +536,95 @@ button:disabled {
display: none;
}
.image-overlay {
position: absolute;
inset: 0;
z-index: 2;
pointer-events: none;
color: rgba(245, 248, 255, 0.86);
font-family: "Consolas", "Cascadia Mono", monospace;
font-size: 14px;
line-height: 1.25;
text-shadow: 0 1px 3px rgba(0, 0, 0, 0.95);
}
.image-overlay.hidden {
display: none;
}
.crosshair {
position: absolute;
opacity: 0.72;
}
.crosshair-v {
top: 4%;
bottom: 4%;
left: 50%;
width: 1px;
background: #ff3b30;
}
.crosshair-h {
left: 3%;
right: 3%;
top: 50%;
height: 1px;
background: #21c55d;
}
.ov {
position: absolute;
max-width: 42%;
white-space: pre-line;
}
.ov-left-top {
top: 12px;
left: 14px;
}
.ov-right-top {
top: 12px;
right: 54px;
text-align: right;
}
.ov-left-mid {
left: 12px;
top: 50%;
transform: translateY(-50%);
}
.ov-right-mid {
right: 54px;
top: 50%;
transform: translateY(-50%);
}
.ov-top-mid {
top: 12px;
left: 50%;
transform: translateX(-50%);
}
.ov-bottom-mid {
bottom: 12px;
left: 50%;
transform: translateX(-50%);
}
.ov-left-bottom {
left: 14px;
bottom: 12px;
}
.ov-right-bottom {
right: 54px;
bottom: 12px;
text-align: right;
}
.slice-rail {
position: absolute;
top: 16px;
@@ -550,9 +673,39 @@ button:disabled {
color: #baf8ee;
}
.source-legend {
display: flex;
gap: 12px;
margin: -2px 0 10px;
color: var(--muted);
font-size: 12px;
}
.source-legend span {
display: flex;
align-items: center;
gap: 5px;
}
.manual-dot,
.ai-dot {
width: 9px;
height: 9px;
display: inline-block;
border-radius: 999px;
}
.manual-dot {
background: var(--cyan);
}
.ai-dot {
background: var(--amber);
}
.part-grid {
display: grid;
grid-template-columns: repeat(6, minmax(82px, 1fr));
grid-template-columns: repeat(7, minmax(78px, 1fr));
gap: 8px;
}
@@ -577,11 +730,29 @@ button:disabled {
background: rgba(25, 212, 194, 0.1);
}
.part-grid label.manual-selected,
.phase-options label.manual-selected {
border-color: rgba(25, 212, 194, 0.72);
box-shadow: inset 3px 0 0 rgba(25, 212, 194, 0.95);
}
.part-grid label.ai-selected,
.phase-options label.ai-selected {
border-color: rgba(240, 181, 78, 0.72);
background: rgba(240, 181, 78, 0.1);
box-shadow: inset 3px 0 0 rgba(240, 181, 78, 0.95);
}
.part-grid .skip-option:has(input:checked) {
border-color: rgba(240, 181, 78, 0.72);
background: rgba(240, 181, 78, 0.12);
}
.part-grid .plain-option:has(input:checked) {
border-color: rgba(52, 116, 246, 0.72);
background: rgba(52, 116, 246, 0.12);
}
.part-grid input:disabled + * {
color: var(--muted);
}
@@ -610,9 +781,6 @@ button:disabled {
}
.annotation-actions {
display: grid;
grid-template-columns: minmax(220px, 1fr) 160px;
gap: 10px;
margin-top: 10px;
}