diff --git a/README.md b/README.md index 4c86d83..0320ebc 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # VoxelMorph Head CT Deformable Registration -面向“患者平扫 CT（中立位）到仰头 CT（极度后仰位）”的 3D 形变配准工程。项目包含 DICOM 转 NIfTI、医学图像预处理、官方 VoxelMorph 训练适配器、独立推理，以及 Streamlit 交互式结果查看界面。 +面向“患者仰头 CT（Moving）到平扫 CT（Fixed）”的 3D 形变配准工程。项目包含 DICOM 转 NIfTI、医学图像预处理、官方 VoxelMorph 训练适配器、独立推理，以及 Streamlit 交互式结果查看界面。 核心模型使用官方仓库 `voxelmorph/voxelmorph` 的 PyTorch 实现： @@ -15,8 +15,8 @@ ```text Voxelmorph_Head_CT/ ├── Data/ -│ ├── 患者1-平扫CT/ # Moving 原始 DICOM -│ ├── 患者1-仰头CT/ # Fixed 原始 DICOM +│ ├── 患者1-平扫CT/ # Fixed 原始 DICOM +│ ├── 患者1-仰头CT/ # Moving 原始 DICOM │ └── 患者2-平扫CT/ ├── app.py # Streamlit Web 可视化界面 ├── config.py # 默认路径与预处理参数 @@ -60,11 +60,11 @@ bash scripts/setup_env.sh ```bash python data_loader.py \ --dicom-dir "Data/患者1-平扫CT" \ - --output "outputs/nifti/patient1_moving.nii.gz" + --output "outputs/nifti/patient1_fixed.nii.gz" python data_loader.py \ --dicom-dir "Data/患者1-仰头CT" \ - --output "outputs/nifti/patient1_fixed.nii.gz" + --output "outputs/nifti/patient1_moving.nii.gz" ``` `data_loader.py` 会优先按 `InstanceNumber` 排序，其次按 `SliceLocation` 排序，并保存 spacing、层厚、排序依据等元数据 JSON。 @@ -72,17 +72,17 @@ python data_loader.py \ ## 2. 预处理 ```bash -python preprocess.py \ - --input "outputs/nifti/patient1_moving.nii.gz" \ - --output "outputs/preprocessed/patient1_moving_preprocessed.nii.gz" \ - --target-spacing 1 1 1 \ - --target-shape 160 192 224 - python preprocess.py \ --input "outputs/nifti/patient1_fixed.nii.gz" \ --output "outputs/preprocessed/patient1_fixed_preprocessed.nii.gz" \ --target-spacing 1 1 1 \ - --target-shape 160 192 224 + --target-shape 256 256 352 + +python preprocess.py \ + --input "outputs/nifti/patient1_moving.nii.gz" \ + --output "outputs/preprocessed/patient1_moving_preprocessed.nii.gz" \ + --target-spacing 1 1 1 \ + --target-shape 256 256 352 ``` 默认窗口为 `W=400, L=40`，适合观察颈部软组织和气道。 @@ -93,10 +93,10 @@ python preprocess.py \ python model_and_train.py \ --moving "outputs/preprocessed/patient1_moving_preprocessed.nii.gz" \ --fixed "outputs/preprocessed/patient1_fixed_preprocessed.nii.gz" \ - --checkpoint "outputs/checkpoints/vxm_head_ct.pt" \ - --epochs 200 \ - --image-loss ncc \ - --ncc-impl local \ + --checkpoint "outputs/checkpoints/vxm_head_ct_patient1.pt" \ + --epochs 80 \ + --image-loss mse \ + --nb-features 8 8 8 8 8 \ --smooth-weight 0.01 ``` @@ -109,7 +109,7 @@ python model_and_train.py \ python infer.py \ --moving "outputs/preprocessed/patient1_moving_preprocessed.nii.gz" \ --fixed "outputs/preprocessed/patient1_fixed_preprocessed.nii.gz" \ - --checkpoint "outputs/checkpoints/vxm_head_ct.pt" \ + --checkpoint "outputs/checkpoints/vxm_head_ct_patient1.pt" \ --out-dir "outputs/inference" ``` @@ -131,9 +131,8 @@ streamlit run app.py 网页提供： -- Moving/Fixed/模型权重/输出目录输入。 -- 自动发现 `outputs/` 与项目目录下的 NIfTI 和 checkpoint，也支持手动输入路径。 -- “开始推理”按钮。 +- 患者1专用固定路径：Fixed 为 `患者1-平扫CT`，Moving 为 `患者1-仰头CT`。 +- “重新训练模型”和“开始推理”按钮。 - Axial、Coronal、Sagittal 正交三视图；每个平面按行同时展示 Fixed、Moving、Warped。 - Fixed 与 Warped 的 Alpha 融合或棋盘格对比。 - DDF 位移强度热力图。 @@ -143,4 +142,4 @@ streamlit run app.py - DICOM 转换和重采样都有 `--max-memory-mb` 防护。 - Web 界面对超大 NIfTI 会通过 nibabel proxy 按 stride 切片读取并下采样，只影响浏览器展示，不改变磁盘结果；侧栏可调整显示体素上限。 -- 训练阶段的主要瓶颈是 3D U-Net 显存；`160x192x224` 是较重的 3D 输入，建议优先使用 CUDA GPU。 +- 训练阶段的主要瓶颈是 3D U-Net 显存；`256x256x352` 是较重的 3D 输入，建议优先使用 CUDA GPU。当前患者1默认使用较轻的 `8 8 8 8 8` 特征配置。 diff --git a/app.py b/app.py index 4458f38..c811c4a 100644 --- a/app.py +++ b/app.py @@ -31,7 +31,7 @@ from metrics import crop_to_common_shape, ddf_summary, registration_metrics, sli st.set_page_config( page_title="VoxelMorph 颈部 CT 配准工作台", layout="wide", - initial_sidebar_state="expanded", + initial_sidebar_state="collapsed", ) @@ -608,31 +608,70 @@ def run_inference_from_ui(moving_path: str, fixed_path: str, checkpoint_path: st ) +def run_training_from_ui(moving_path: str, fixed_path: str, checkpoint_path: str) -> None: + from model_and_train import train_pair + + train_pair( + moving_path=moving_path, + fixed_path=fixed_path, + checkpoint_path=checkpoint_path, + epochs=80, + learning_rate=1e-4, + smooth_weight=0.01, + image_loss="mse", + nb_features=(8, 8, 8, 8, 8), + save_every=20, + ) + + def main() -> None: st.markdown(CUSTOM_CSS, unsafe_allow_html=True) st.title("VoxelMorph 颈部 CT 配准工作台") - candidates = discover_nifti_files() - checkpoint_candidates = discover_checkpoint_files() - with st.sidebar: - st.header("输入") - moving_path = choose_path("Moving", DEFAULT_MOVING_NIFTI, candidates) - fixed_path = choose_path("Fixed", DEFAULT_FIXED_NIFTI, candidates) - checkpoint_path = choose_path("模型权重", DEFAULT_CHECKPOINT, checkpoint_candidates) - out_dir = st.text_input("输出目录", value=str(INFERENCE_DIR)) - display_max_voxels = st.slider("Web显示体素上限", 2_000_000, 30_000_000, 14_000_000, 1_000_000) + moving_path = str(DEFAULT_MOVING_NIFTI) + fixed_path = str(DEFAULT_FIXED_NIFTI) + checkpoint_path = str(DEFAULT_CHECKPOINT) + out_dir = str(INFERENCE_DIR) + display_max_voxels = 30_000_000 - start = st.button("开始推理", type="primary", width="stretch") - if start: - with st.spinner("推理运行中"): - try: - load_nifti_cached.clear() - result = run_inference_from_ui(moving_path, fixed_path, checkpoint_path, out_dir) - load_nifti_cached.clear() - st.session_state["last_result"] = result - st.success("推理完成") - except Exception as exc: - st.error(str(exc)) + st.caption( + "患者1专用：固定图像 = 平扫CT；移动图像 = 仰头CT。" + "推理前会重采样、归一化并裁剪/填充到同一模型网格。" + ) + info_cols = st.columns(4) + info_cols[0].metric("Fixed", "患者1-平扫CT") + info_cols[1].metric("Moving", "患者1-仰头CT") + info_cols[2].metric("模型", Path(checkpoint_path).name) + info_cols[3].metric("输出", Path(out_dir).name) + + action_cols = st.columns([1, 1, 4]) + with action_cols[0]: + train_now = st.button("重新训练模型", type="primary", width="stretch") + with action_cols[1]: + start = st.button("开始推理", width="stretch") + + if train_now: + with st.spinner("患者1模型训练中"): + try: + load_nifti_cached.clear() + run_training_from_ui(moving_path, fixed_path, checkpoint_path) + result = run_inference_from_ui(moving_path, fixed_path, checkpoint_path, out_dir) + load_nifti_cached.clear() + st.session_state["last_result"] = result + st.success("训练和推理完成") + except Exception as exc: + st.error(str(exc)) + + if start: + with st.spinner("推理运行中"): + try: + load_nifti_cached.clear() + result = run_inference_from_ui(moving_path, fixed_path, checkpoint_path, out_dir) + load_nifti_cached.clear() + st.session_state["last_result"] = result + st.success("推理完成") + except Exception as exc: + st.error(str(exc)) result_paths = load_result_paths(out_dir) last_result = st.session_state.get("last_result", {}) diff --git a/config.py b/config.py index f7faa94..8ee094f 100644 --- a/config.py +++ b/config.py @@ -10,8 +10,8 @@ from pathlib import Path PROJECT_ROOT = Path(__file__).resolve().parent DATA_ROOT = PROJECT_ROOT / "Data" -DEFAULT_MOVING_DICOM_DIR = DATA_ROOT / "患者1-平扫CT" -DEFAULT_FIXED_DICOM_DIR = DATA_ROOT / "患者1-仰头CT" +DEFAULT_FIXED_DICOM_DIR = DATA_ROOT / "患者1-平扫CT" +DEFAULT_MOVING_DICOM_DIR = DATA_ROOT / "患者1-仰头CT" OUTPUT_ROOT = PROJECT_ROOT / "outputs" NIFTI_DIR = OUTPUT_ROOT / "nifti" @@ -21,10 +21,10 @@ INFERENCE_DIR = OUTPUT_ROOT / "inference" DEFAULT_MOVING_NIFTI = PREPROCESSED_DIR / "patient1_moving_preprocessed.nii.gz" DEFAULT_FIXED_NIFTI = PREPROCESSED_DIR / "patient1_fixed_preprocessed.nii.gz" -DEFAULT_CHECKPOINT = CHECKPOINT_DIR / "vxm_head_ct.pt" +DEFAULT_CHECKPOINT = CHECKPOINT_DIR / "vxm_head_ct_patient1.pt" # VoxelMorph 的 3D U-Net 多次下采样，三维尺寸建议均为 16 的倍数。 -DEFAULT_TARGET_SHAPE = (160, 192, 224) # NIfTI 轴顺序: X, Y, Z +DEFAULT_TARGET_SHAPE = (256, 256, 352) # NIfTI 轴顺序: X, Y, Z DEFAULT_TARGET_SPACING = (1.0, 1.0, 1.0) # mm, X/Y/Z # 颈部软组织/气道观察常用窗口：W=400, L=40。 diff --git a/outputs/checkpoints/vxm_head_ct_patient1.history.json b/outputs/checkpoints/vxm_head_ct_patient1.history.json new file mode 100644 index 0000000..b604be2 --- /dev/null +++ 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