Xiao Furen
523ec7ee16
Introduces a new scoring component for vertebral body (VBODY) rewards to improve optimization accuracy. The update includes: - Added `vbody_tensor` to `OptimizationContext` and scoring functions to reward screw placement within the vertebral body. - Enhanced `segment_spinous_process` with diagnostic capabilities to detect spinous process absence (e.g., post-laminectomy). - Improved `resample_img` to prevent physical boundary clipping and handle interpolation more robustly for CT and label data. - Implemented a metadata cache using TinyDB in the preprocessing pipeline to skip low-resolution or insufficient scans efficiently. - Added robust error handling for NFS-based file operations and directory creation. - Added new visualization tools for bone figures and level plotting. refactor(imaging): improve segmentation and resampling precision - Refactored `seg_bone` to support original resolution binary masks and Signed Maurer Distance Maps (SMD) for more accurate boundary handling. - Updated `resample_img` to use `ceil` for output size calculation to ensure full physical coverage. - Optimized `process_single_image` to utilize metadata for skipping processing of invalid or low-quality scans.
310 lines
No EOL
14 KiB
Python
310 lines
No EOL
14 KiB
Python
#!/home/xfr/.conda/envs/cbt/bin/python
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"""
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檢查各 (volume, level) 的棘突（midline 後側構造）是否缺如
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（先前手術如 laminectomy / 棘突切除，例：0005 L5、0770 L5）。
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判定原理（見 imaging.orientation.diagnose_spinous_process）：
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棘突完整時，尖端是全椎體最後側的骨且位於中線 →
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deficit（全骨最後側 - 中線帶最後側）≈ 0，後側中線窄帶有骨。
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切除後，最後側骨偏到側方殘餘、後側中線空洞 → deficit 大 + rear3 ≈ 0。
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no_spinous = deficit >= 4 vox 且 rear3 <= 20 vox（正常 level deficit 恒為 0）。
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兩種模式：
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quick（預設，掃全資料集用）→ 兩段式：
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stage 1：axial 投影快速篩檢（~0.5s/level）。用「寬行（椎體行）」擬合
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中線軌跡（對傾斜/旋轉穩健，不受後側殘片污染），找出「缺棘突候選」。
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高召回（幾乎不漏）、但因純投影在傾斜脊椎上仍會有少數假候補。
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stage 2：僅對候選跑 full（鏡稱面）驗證（~3s/候選），以 full 為準給出
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最終判定（confirmed / false alarm）。全資料集 ~25min + 候數×3s。
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full（指定單一 volume+level 時預設）：跑鏡稱面 + 棘突/椎體完整分割，
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另回報 VBODY 分割 mode（nosp_gap / nosp_post_min / quantile...），
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確認該 level 的椎體切分落在「椎體後側末端（體/弓最薄處）」。
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識別出的 no_spinous level 在 xfr_debug.py 執行時會打 [NO-SP] 標記，
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並自動改用放寬後側谷底的椎體切分（不再把殘留後側要素當棘突移除）。
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Usage:
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python xfr_check_spinous.py # quick，全部 volume L1~L5
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python xfr_check_spinous.py 0005 # quick，該 volume L1~L5
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python xfr_check_spinous.py 0005 L5 # full（精確）單一 level
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python xfr_check_spinous.py --full 0005 L5 # 強制 full
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python xfr_check_spinous.py --quick # 強制 quick（全資料集掃）
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"""
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import csv
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import os
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import sys
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import time
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import numpy as np
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import SimpleITK as sitk
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from imaging.orientation import (best_symmetry_plane, segment_spinous_process,
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best_upper_endplate_plane, segment_vertebral_body,
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diagnose_spinous_process)
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standardized_dir = '/mnt/1248/open2/cyrou/CBT/Seg/Resample/standardized-xfr/'
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LEVELS = ('L1', 'L2', 'L3', 'L4', 'L5')
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LOG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'results')
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# 與 diagnose_spinous_process 相同的判定閾值
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DEFICIT_MIN = 4.0
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REAR3_MAX = 20
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# quick：中線帶內一行的最小 bone voxel 數（< 此值視為殘片游離 voxel，不算「尖端在」）
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MIN_MID_VOX = 3
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# =====================================================================
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# quick screen：不依賴鏡稱面（~0.3s/level）
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# =====================================================================
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def quick_screen(m):
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"""axial 投影 (y, x) 快速篩檢（不跑鏡稱面，對傾斜/旋轉穩健）：
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- 「寬行」= 行內骨 voxel 數 >= 最大行的 50%（即椎體行；椎體左右
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大致對稱 → 寬行 x 重心 ≈ 中線上的點）。
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- 中線軌跡 x(y) = p0 + p1·y 只用寬行（椎體行）擬合：
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* 用全部行擬合 → laminectomy 後側殘片行污染中線，deficit 被吃掉
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（0005 L5 假正常）；
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* 用固定垂直中線 → 傾斜棘突尖端被誤判為側方偏移
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（0555/0609/0637 假切除）。椎體行兩者都不沾，故最穩。
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- deficit = 最後側骨行 (y_min) 到「中線軌跡 ± 25% 椎體寬度 band
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內有骨的最後側行」的行距（尖端在中線上 → 0；切除後 → ≈切除量）。
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- rear3/rear6 = 最後側 3/6 行內、中線軌跡 ± 10% 寬度（窄中線帶）的
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骨 voxel 數。
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- top_off = 最後側一行 x 重心到中線軌跡的距離。
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回傳 dict 或 None（資料不足）。"""
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proj = m.max(axis=0)
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ys_p, xs_p = np.where(proj > 0)
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if ys_p.size < 50:
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return None
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ny = proj.shape[0]
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cnt = np.bincount(ys_p, minlength=ny)
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max_cnt = int(cnt.max())
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if max_cnt < 4:
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return None
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xsum = np.bincount(ys_p, weights=xs_p.astype(np.float64), minlength=ny)
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centroid = xsum / cnt.astype(float)
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wide = cnt >= (0.5 * max_cnt) # 椎體行
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if int(wide.sum()) < 10:
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return None
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yv = np.where(wide)[0]
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p1, p0 = np.polyfit(yv, centroid[wide], 1) # 中線軌跡（椎體行擬合）
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xmax = np.full(ny, -1.0)
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xmin = np.full(ny, np.inf)
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np.maximum.at(xmax, ys_p, xs_p)
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np.minimum.at(xmin, ys_p, xs_p)
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span_c = float(np.median(xmax[yv] - xmin[yv]))
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if span_c < 10:
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return None
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line = p0 + p1 * ys_p
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band = 0.25 * span_c # central band（椎體中央區）
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narrow = 0.10 * span_c # 窄中線帶
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y_min = int(ys_p.min())
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# 「中線有骨」需 >= MIN_MID_VOX 個 voxel 才算數：棘突尖端在帶內有
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# 多個 voxel，切除後的側方殘片在帶內通常只有 1-2 個游離 voxel
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# （否則 0005 L5 的單 voxel 殘端會被當成尖端 → 假正常）。
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bcnt = np.bincount(ys_p[np.abs(xs_p - line) <= band], minlength=ny)
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idx = np.where(bcnt >= MIN_MID_VOX)[0]
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y_mid = int(idx.min()) if idx.size else ny - 1
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deficit = float(y_mid - y_min)
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nn = np.abs(xs_p - line) <= narrow
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rear3 = int(((ys_p <= y_min + 3.0) & nn).sum())
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rear6 = int(((ys_p <= y_min + 6.0) & nn).sum())
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top_off = float(abs(xs_p[ys_p == y_min].mean() - (p0 + p1 * y_min)))
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return {'deficit': deficit, 'rear3': rear3, 'rear6': rear6,
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'top_off': top_off, 'no_spinous': bool(deficit >= DEFICIT_MIN
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and rear3 <= REAR3_MAX)}
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# =====================================================================
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# full：鏡稱面 + 完整棘突/椎體分割（精確，較慢）
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# =====================================================================
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def full_check(m):
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sym = best_symmetry_plane(m)
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diag = diagnose_spinous_process(m, sym)
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sp_mask, sp_th, sp_info = segment_spinous_process(m, sym)
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ep = best_upper_endplate_plane(m)
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vb, vb_th, vb_info = segment_vertebral_body(m, sym, ep, sp_th, sp_info['mode'])
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row = {'deficit': float(diag['deficit']), 'rear3': diag['rear3'],
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'rear6': diag['rear6'], 'top_off': diag['top_off'],
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'no_spinous': bool(diag['no_spinous']),
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'sp_mode': sp_info['mode'],
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'sp_pct': 100.0 * sp_info['n_sp'] / max(int(m.sum()), 1),
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'vb_mode': vb_info['mode'],
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'vb_pct': (100.0 * vb_info['n_vb'] / max(int(m.sum()), 1))
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if vb is not None else None,
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'vb_ap_th': vb_th}
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return row
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# =====================================================================
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def load_binary(volume_dir, level):
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# _binary.nii.gz 現為原解析度（未插值）；0.5mm 分析優 _binary_sdf（SDF 平滑遮罩）
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p = os.path.join(volume_dir, f'{level}_binary_sdf.nii.gz')
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if not os.path.exists(p):
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p = os.path.join(volume_dir, f'{level}_binary.nii.gz')
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if not os.path.exists(p):
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return None
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m = sitk.GetArrayFromImage(sitk.ReadImage(p, sitk.sitkUInt8)) > 0
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if int(m.sum()) < 1000 or min(m.shape) < 8:
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return None
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return m
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def check_level(vid, level, use_full):
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m = load_binary(os.path.join(standardized_dir, vid), level)
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if m is None:
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return {'vid': vid, 'level': level, 'note': 'missing/degenerate mask'}
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t0 = time.time()
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row = quick_screen(m) if not use_full else full_check(m)
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row.update(vid=vid.rsplit('.', 1)[-1], level=level,
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n_bone=int(m.sum()), sec=round(time.time() - t0, 2))
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return row
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def parse_args(argv):
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use_full = None
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pos = []
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for a in argv:
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if a == '--full':
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use_full = True
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elif a == '--quick':
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use_full = False
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else:
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pos.append(a)
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if len(pos) > 2:
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sys.exit('Usage: python xfr_check_spinous.py [--full|--quick] [volume_id] [level]')
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vid_arg = pos[0] if len(pos) >= 1 else None
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level_arg = pos[1] if len(pos) >= 2 else None
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if level_arg and level_arg.upper() not in LEVELS:
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sys.exit(f'Invalid level: {level_arg} (choose from {"/".join(LEVELS)})')
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if level_arg and not vid_arg:
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sys.exit('level requires volume_id')
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if use_full is None:
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use_full = bool(vid_arg and level_arg) # 指定單 level → 精確模式
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return vid_arg, (level_arg.upper() if level_arg else None), use_full
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def main():
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os.makedirs(LOG_DIR, exist_ok=True)
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vid_arg, level_arg, use_full = parse_args(sys.argv[1:])
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volumes = [d for d in sorted(os.listdir(standardized_dir))
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if os.path.isdir(os.path.join(standardized_dir, d))]
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if vid_arg is not None:
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key = vid_arg.lower()
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vols = [v for v in volumes
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if v.lower() == key or v.rsplit('.', 1)[-1] == key]
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if not vols:
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sys.exit(f'Volume not found: {vid_arg}')
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volumes = vols
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levels = (level_arg,) if level_arg else LEVELS
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tasks = [(v, lvl) for v in volumes for lvl in levels]
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mode = ('FULL (mirror plane)' if use_full
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else 'QUICK screen -> FULL-verify candidates (2-stage)')
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print(f'Mode: {mode} | {len(volumes)} volumes x {len(levels)} levels = {len(tasks)} tasks',
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flush=True)
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t0 = time.time()
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rows = []
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candidates = [] # (row 索引, volume 目錄名)
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for i, (vid, lvl) in enumerate(tasks, 1):
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try:
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row = check_level(vid, lvl, use_full)
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except Exception as e:
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row = {'vid': vid.rsplit('.', 1)[-1], 'level': lvl, 'note': f'error: {e}'}
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rows.append(row)
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if 'note' in row:
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print(f"[{i}/{len(tasks)}] {row['vid']} {row['level']}: {row['note']}", flush=True)
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elif use_full:
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top_str = 'n/a' if row['top_off'] is None else f"{row['top_off']:.1f}"
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print(f"[{i}/{len(tasks)}] {row['vid']} {row['level']}: "
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f"deficit={row['deficit']:6.1f} rear3={row['rear3']} "
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f"top_off={top_str} {'<<< NO-SPINUS' if row['no_spinous'] else 'ok'} "
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f"sp={row['sp_mode']} {row['sp_pct']:.1f}% "
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f"vb={row['vb_mode']} {row['vb_pct']:.1f}%", flush=True)
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else:
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mark = 'candidate' if row['no_spinous'] else 'ok'
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print(f"[{i}/{len(tasks)}] {row['vid']} {row['level']}: "
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f"deficit={row['deficit']:6.1f} rear3={row['rear3']:4d} "
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f"rear6={row['rear6']:5d} top_off={row['top_off']:5.1f} {mark}",
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flush=True)
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if not use_full and row.get('no_spinous'):
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candidates.append((len(rows) - 1, vid))
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# ---- Stage 2（quick 模式）：候選用 full（鏡稱面）驗證，以 full 為準 ----
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flagged = []
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cleared = []
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if use_full:
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for r in rows:
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if r.get('no_spinous'):
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flagged.append(f"{r['vid']} {r['level']}")
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elif candidates:
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print(f'\n=== stage 2: {len(candidates)} quick candidate(s) -> '
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f'full (mirror plane) verification ===', flush=True)
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for j, (k, vid_dir) in enumerate(candidates, 1):
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row = rows[k]
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try:
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m = load_binary(os.path.join(standardized_dir, vid_dir), row['level'])
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frow = full_check(m) if m is not None else {}
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except Exception as e:
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frow = {'note': f'verify error: {e}'}
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for kk in ('deficit', 'rear3', 'rear6', 'top_off', 'no_spinous',
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'sp_mode', 'sp_pct', 'vb_mode', 'vb_pct', 'vb_ap_th'):
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if kk in frow:
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row[kk] = frow[kk]
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if not frow:
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flag_str = '-> UNVERIFIED (mask missing/full failed)'
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cleared.append(f"{row['vid']} {row['level']} (UNVERIFIED)")
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elif row.get('no_spinous'):
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flag_str = '<<< NO-SPINUS (confirmed)'
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flagged.append(f"{row['vid']} {row['level']}")
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else:
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flag_str = '-> normal (quick false alarm)'
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cleared.append(f"{row['vid']} {row['level']} "
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f"(full deficit={row.get('deficit', float('nan')):.1f})")
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to = row.get('top_off')
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print(f"[verify {j}/{len(candidates)}] {row['vid']} {row['level']}: "
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f"deficit={row.get('deficit', float('nan')):6.1f} "
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f"rear3={row.get('rear3', '?')} "
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f"top_off={'n/a' if to is None else format(to, '5.1f')} "
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f"sp={row.get('sp_mode', '?')} "
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f"vb={row.get('vb_mode', '?')} {row.get('vb_pct') or 0:.1f}% {flag_str}",
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flush=True)
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# ---- CSV ----
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ts = time.strftime('%Y%m%d_%H%M%S')
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fields = ['vid', 'level', 'n_bone', 'deficit', 'rear3', 'rear6', 'top_off',
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'no_spinous', 'sp_mode', 'sp_pct', 'vb_mode', 'vb_pct', 'vb_ap_th',
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'sec', 'note']
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csv_path = os.path.join(LOG_DIR, f'spinous_check_{ts}.csv')
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with open(csv_path, 'w', newline='') as f:
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wtr = csv.DictWriter(f, fieldnames=fields, extrasaction='ignore')
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wtr.writeheader()
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for r in rows:
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r['vid'] = r.get('vid', '')
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wtr.writerow(r)
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dt = (time.time() - t0) / 60.0
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print('\n' + '=' * 64)
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print(f'Done in {dt:.1f} min | {len(rows)} levels checked (mode: {mode})')
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if flagged:
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print(f'\n>>> {len(flagged)} level(s) with NO SPINOUS PROCESS (prior laminectomy/resection):')
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for f_ in flagged:
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print(f' {f_}')
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print(' (xfr_debug.py 執行這些 level 時會打 [NO-SP] 並自動改用放寬後側谷底的椎體切分)')
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else:
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print('\nNo missing spinous process detected.')
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if cleared:
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print(f' (quick 候選、經 full 驗證為正常: {", ".join(cleared)})')
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if use_full:
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quant = [f"{r['vid']} {r['level']}" for r in rows
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if r.get('vb_mode') == 'quantile']
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if quant:
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print(f'\nWARNING: VBODY 退回 55 百分位切分（可能切進椎體）: {", ".join(quant)}')
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print(f'CSV: {csv_path}')
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if __name__ == '__main__':
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main() |