refactor(imaging): update coordinate transformation logic to use transform.json
Update the coordinate transformation pipeline to prioritize the affine transformation chain recorded in `transform.json` (original_to_source) instead of relying on re-calculated geometric parameters. The previous method relied on re-estimating rotation, center, and bounding boxes from the bone mask, which led to inaccuracies in screw pillar mapping (e.g., dropping from 99% to 30-94% in-label accuracy). The new approach uses the precise inverse affine mapping `o = M^-1 (s - t)` from the transformation metadata. A fallback mechanism is maintained for legacy volumes lacking `transform.json`, which continues to use the re-calculation method. - Implement `load_transform` and `original_to_source` integration - Update documentation to reflect the new primary/fallback coordinate chains - Improve precision of screw parameter mapping to native index space
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1 changed files with 69 additions and 29 deletions
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@ -8,27 +8,34 @@
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label 值：L1L=1 L1R=2 L2L=3 L2R=4 L3L=5 L3R=6 L4L=7 L4R=8 L5L=9 L5R=10
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（0 = 背景）。
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座標鏈（純 index 空間；不依賴 standardized 輸出的物理 header ——
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standardize_affine 的 origin 處理不可靠，2026-09-08 已驗證）：
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rotated disk r（(x,y,z) index）
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-> template 0.5mm: t = R^T (r + fstart - c) + c [行向量: (r+fstart-c) @ R + c]
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-> 記憶體 0.5mm 全域: g0 = bbox2s + (wx-1-tx, wy-1-ty, tz)
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-> ap_flip 時: g0y = N05y - 1 - g0y
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-> native index: rint(g0 * 0.5 / sn)
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座標鏈首選：直接用預處理寫檔時記錄在 <vol>/transform.json 的正向鏈
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（imaging.transforms.original_to_source：boxes / std_flip_axes / ap_flip /
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rotated R、center、start / 原 CT 幾何；純 index 空間、不依賴 standardized
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輸出的物理 header）。該鏈為仿射（original index <-> rotated disk index），
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螺絲點經仿射逆向 o = M^-1 (s - t) 精確映射——不重新估算任何平面 / 幾何、
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不重取樣 native label。
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其中每 level 的 R/c 由 template 骨頭 mask（smd_resampled < 0.5，與
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_write_rotated_level 的輸入同定義；退化時退回 _binary_nn）重算；
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fstart = rotated 檔 origin 反映到 template index 的整數；
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bbox2 = native label 線性重取樣 0.5mm（>0.5）的最大 26-連通區域 bbox，
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與 seg_bone 同定義（template/roi/binary_sdf 都裁在這個 bbox2 上）。
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fallback（僅舊世代 volume 沒有 transform.json、或該 level 沒有 rotated
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記錄時）：純 index 空間重算 R/center（template 骨頭 mask 的
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best_symmetry_plane / best_upper_endplate_plane）、fstart（rotated 檔
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origin 反映到 template index 的整數）、bbox2（native label 0.5mm 線性
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重取樣 >0.5 的最大 26-連通區域 bbox）：
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rotated disk r -> t = R^T (r + fstart - c) + c
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-> g0 = bbox2s + (wx-1-tx, wy-1-ty, tz)；ap_flip 時 g0y = N05y-1-g0y
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-> native: rint(g0 * 0.5 / sn)
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重算結果可能與生成時參數不完全一致：2026-09-09 驗證 liver_100 螺絲柱
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in-label 由 93-99%（transform.json）掉到 30-94%（重算）；新世代資料
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（transform.json 在）一律走主路徑。
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螺絲參數化（rotated 系、0.5mm index）：pos = [z, y, x, az°, alt°, d mm, L mm]；
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方向 d_v = (cos az sin alt, sin az sin alt, cos alt)；末端 = p0 + L/0.5 * d_v；
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柱半徑 = d/2 mm。
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驗證（2026-09-08）：整顆骨頭 15/15 (volume, level) 100% 落在 native label
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（/tmp/kilo/validate_final.py）；螺絲柱體全部點 20/20 level-side
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in-label >= 96%（2-voxel 膨脹）（/tmp/kilo/validate_screws2.py）。
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驗證（2026-09-09，transform.json 路徑）：20260909_141448 run —
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0001（10/10）、liver_100（10/10）、covid19（L1/L2 六側）螺絲柱體
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in-label >= 92%、2-voxel 膨脹 >= 98%。covid19 L3 兩側除外：該 volume 的
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L3 旋轉 bone mask 是 4.5k voxel / 5 slice 的退化區域（L4/L5 預處理缺檔、
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先前棘突切除），屬資料問題非映射問題（L3 bone 碎片本身逆向 in-label 97.8%）。
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"""
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import argparse
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@ -45,7 +52,8 @@ from config.constant import LABEL_MAP
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from imaging.resample import resample_img
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from imaging.segmentation import _largest_cc_bbox
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from imaging.orientation import best_symmetry_plane, best_upper_endplate_plane
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from imaging.transforms import level_file_path
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from imaging.transforms import (level_file_path, load_transform,
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original_to_source, transform_path)
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from visualization.res_bone_figure import compute_normalizing_rotation
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standardized_dir = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr-3'
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@ -176,15 +184,32 @@ def write_volume_cbt(volume_id, run_id, output_root=Output_dir, date=None):
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ct_path, lb_path, ap_flip = find_native_paths(volume_id)
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ct = sitk.ReadImage(ct_path)
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lb_img = sitk.ReadImage(lb_path)
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lb_arr = sitk.GetArrayFromImage(lb_img)
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Nn = np.array(ct.GetSize(), float)
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n_xyz = np.array(ct.GetSize(), int) # (x,y,z)
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n_zyx = n_xyz[::-1].copy() # 輸出 (z,y,x)
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sn = np.array(ct.GetSpacing(), float)
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N05 = np.maximum(1, np.ceil(Nn * sn / 0.5 - 1e-6).astype(int))
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N05 = np.maximum(1, np.ceil(n_xyz.astype(float) * sn / 0.5 - 1e-6).astype(int))
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out_arr = np.zeros(lb_arr.shape, np.uint8)
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geom_cache = {}
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# 主路徑：transform.json 記錄的正向鏈（仿射），逆向 o = M^-1 (s - t)
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meta = None
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try:
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meta = load_transform(os.path.join(standardized_dir, volume_id))
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except FileNotFoundError:
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logger.warning(f'{volume_id}: no transform.json '
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f'({transform_path(os.path.join(standardized_dir, volume_id))}); '
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f'全部 level 退回重算路徑（近似）')
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if meta is not None and not np.array_equal(
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np.asarray(meta['original']['size'], int), n_xyz):
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logger.warning(f'{volume_id}: transform.json original size '
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f'{meta["original"]["size"]} != CT {list(n_xyz)}; '
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f'忽略 transform.json，退回重算路徑')
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meta = None
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out_arr = np.zeros(n_zyx, np.uint8)
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lb_img = lb_arr = None # fallback 才需要讀 native label
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geom_cache = {} # fallback：每 level 的 (R, c, fstart, bbox2s, wx, wy)
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aff_cache = {} # 主路徑：每 level 的 (M^-1, t)
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n_screws, skipped = 0, []
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n_tf = n_fb = 0
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for li, level in enumerate(LEVELS):
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for side in ('L', 'R'):
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jp = os.path.join(side_vol_dir, f'{level}_{side}.json')
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@ -192,15 +217,29 @@ def write_volume_cbt(volume_id, run_id, output_root=Output_dir, date=None):
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continue
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pos = json.load(open(jp))['position']
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try:
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if level not in geom_cache:
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geom_cache[level] = level_geometry(volume_id, level, lb_img, lb_arr)
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geom = geom_cache[level]
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cyl = screw_voxel_xyz(pos)
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nat = rotated_to_native(cyl, geom, N05, sn, ap_flip)
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lv = meta['levels'].get(level) if meta is not None else None
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if lv is not None and 'rotated' in lv:
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if level not in aff_cache:
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M, t = original_to_source(meta, level, 'rotated')
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aff_cache[level] = (np.linalg.inv(M), t)
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Mi, t = aff_cache[level]
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nat = (cyl - t) @ Mi.T # (N,3) 原 CT 連續 index
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n_tf += 1
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else:
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if lb_arr is None:
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lb_img = sitk.ReadImage(lb_path)
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lb_arr = sitk.GetArrayFromImage(lb_img)
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if level not in geom_cache:
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geom_cache[level] = level_geometry(volume_id, level, lb_img, lb_arr)
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nat = rotated_to_native(cyl, geom_cache[level], N05, sn, ap_flip)
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n_fb += 1
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ni = np.rint(nat).astype(int)
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valid = (ni >= 0).all(1) & (ni < np.array(lb_arr.shape[::-1])).all(1)
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valid = (ni >= 0).all(1) & (ni < n_xyz).all(1)
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idx = ni[valid]
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val = li * 2 + (1 if side == 'R' else 0)
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if idx.shape[0] == 0:
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raise ValueError('all screw voxels out of original CT bounds')
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val = li * 2 + 1 + (1 if side == 'R' else 0)
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out_arr[idx[:, 2], idx[:, 1], idx[:, 0]] = val
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n_screws += 1
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except Exception as e:
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@ -217,7 +256,8 @@ def write_volume_cbt(volume_id, run_id, output_root=Output_dir, date=None):
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sitk.WriteImage(out_img, out_path)
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if skipped:
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logger.warning(f'{volume_id}: failed sides: {", ".join(skipped)}')
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logger.info(f'{volume_id}: {n_screws}/10 screws -> {out_path} (ap_flip={ap_flip})')
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logger.info(f'{volume_id}: {n_screws}/10 screws -> {out_path} '
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f'(transform.json={n_tf}, re-est={n_fb}, ap_flip={ap_flip})')
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return out_path, n_screws
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