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.
50 lines
No EOL
2.7 KiB
Python
50 lines
No EOL
2.7 KiB
Python
import numpy as np
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import SimpleITK as sitk
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from config.constant import LABEL_MAP
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def resample_img(sitk_image, out_spacing=[0.5, 0.5, 0.5], is_label=False,
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interpolator=None, cval=None):
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"""重採樣到 out_spacing，完整保留物理範圍（邊界 margin 不裁剪）：
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- CT（is_label=False）：线性插值。本流程是上採樣（~0.75-1mm -> 0.5mm），
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线性即無漣波的理想重建；B-spline 會在身體 margin 等強邊界產生
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非物理的 undershoot/overshoot（量測：~0.5% 體素低於 air floor
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-1024 HU，最低 -1274，沿體輪廓形成黑暈）。
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- 預設填充值 = 影像最小值（air），而非 GetPixelIDValue()
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（對 int16 CT 回傳佔位值 2 ≈ 軟組織，會把 margin 填成軟組織）。
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- 輸出尺寸用 ceil 覆蓋原物理尺寸（round 會讓遠端端點被裁 ≤0.25mm）。
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label（is_label=True）：最近邻、填充 0。
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interpolator：明確指定插值器（None = 依 is_label 取 Linear / NearestNeighbor；
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例如 SMD 等 piecewise-linear 場用 sitk.sitkBSpline，三阶 B 样条对分段
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线性场为精确重建、無漣波）。
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cval：明確指定填充值（None = 上述預設）。
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"""
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original_spacing = np.array(sitk_image.GetSpacing(), dtype=float)
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original_size = np.array(sitk_image.GetSize())
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out_spacing = np.array(out_spacing, dtype=float)
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physical = original_size * original_spacing
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out_size = [max(1, int(np.ceil(physical[i] / out_spacing[i] - 1e-6)))
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for i in range(3)]
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resample = sitk.ResampleImageFilter()
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resample.SetOutputSpacing(out_spacing.tolist())
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resample.SetSize(out_size)
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resample.SetOutputDirection(sitk_image.GetDirection())
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resample.SetOutputOrigin(sitk_image.GetOrigin())
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resample.SetTransform(sitk.Transform())
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if is_label:
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resample.SetInterpolator(interpolator or sitk.sitkNearestNeighbor)
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resample.SetDefaultPixelValue(cval if cval is not None else 0)
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elif interpolator is not None:
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resample.SetInterpolator(interpolator)
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# SMD 等 signed 場的填充：預設 0 = 表面層值（比影像 min/max 安全，
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# 不會製造假的零穿越環）；可用品值可用 cval 覆蓋
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resample.SetDefaultPixelValue(float(cval) if cval is not None else 0.0)
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else:
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resample.SetInterpolator(sitk.sitkLinear)
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# air 值 = 影像最小 HU（statistics 濾波器 streaming 計算，不載入整張 array）
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stats = sitk.StatisticsImageFilter()
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stats.Execute(sitk_image)
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resample.SetDefaultPixelValue(float(stats.GetMinimum()))
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return resample.Execute(sitk_image) |