108 lines
No EOL
3.7 KiB
Python
108 lines
No EOL
3.7 KiB
Python
import os
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import SimpleITK as sitk
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from config.constant import LABEL_MAP
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import numpy as np
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"""
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# 沿用原本 LABEL_MAP
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seg_bone(n, name, img, lbl)
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# user 自定義
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my_map = {1: "L1", 2: "L2", 3: "L3"}
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seg_bone(n, name, img, lbl, label_map=my_map)
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"""
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def seg_bone(n, name, resampled_sitk_img, resampled_sitk_lbl, output_base=None, label_map=LABEL_MAP):
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if output_base==None:
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output_base=='Dataset'
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if n not in label_map:
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raise ValueError(f"Label {n} not found in label_map")
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label_name = label_map[n]
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# lssif = sitk.LabelShapeStatisticsImageFilter()
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# lssif.Execute(resampled_sitk_lbl)
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# if not lssif.HasLabel(n):
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# raise RuntimeError(f"Label {n} not found")
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# bbox2 = lssif.GetBoundingBox(n)
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# 1. 提取標籤 n 的二值遮罩 (將標籤 n 設為 1，其餘為 0)
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binary_mask = sitk.BinaryThreshold(resampled_sitk_lbl, n, n, 1, 0)
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# 2. 獲取所有連通區域
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# 連通區域濾波器會將 binary_mask 中的不同物體標記為 1, 2, 3...
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cc_image = sitk.ConnectedComponent(binary_mask)
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# 3. 根據區域大小（像素/體積）重新標記
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# RelabelComponent 會按大小排序，最大的物體標籤會被設為 1
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relabeled_cc = sitk.RelabelComponent(cc_image, sortByObjectSize=True)
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largest_mask = relabeled_cc == 1
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# 4. 計算形狀統計信息
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shape_stats = sitk.LabelShapeStatisticsImageFilter()
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shape_stats.Execute(relabeled_cc)
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# 檢查是否有找到任何組件
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if shape_stats.GetNumberOfLabels() < 1:
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return None
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# 5. 獲取最大組件（標籤為 1）的邊界框
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# 格式通常為 [x_start, y_start, z_start, x_size, y_size, z_size]
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bbox2 = shape_stats.GetBoundingBox(1)
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roi = sitk.RegionOfInterest(resampled_sitk_img, bbox2[3:], bbox2[:3])
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roi_path = os.path.join(output_base, f"{label_name}_roi.nii.gz")
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sitk.WriteImage(roi, roi_path)
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# label2 = sitk.RegionOfInterest(resampled_sitk_lbl, bbox2[3:], bbox2[:3])
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# binary = sitk.BinaryThreshold(label2, lowerThreshold=n, upperThreshold=n, outsideValue=0, insideValue=1)
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binary = sitk.RegionOfInterest(largest_mask, bbox2[3:], bbox2[:3])
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binary_path = os.path.join(output_base, f"{label_name}_binary.nii.gz")
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sitk.WriteImage(binary, binary_path)
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# roi_pixel_type = roi.GetPixelID()
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# binary_cast = sitk.Cast(binary, roi_pixel_type)
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# roi2 = roi * binary_cast
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roi2 = sitk.Mask(roi, binary)
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roi2_path = os.path.join(output_base, f"{label_name}_roi2.nii.gz")
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sitk.WriteImage(roi2, roi2_path)
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# lsif = sitk.LabelStatisticsImageFilter()
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# label2_int = sitk.Cast(label2, sitk.sitkUInt16)
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# lsif.Execute(roi2, label2_int)
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# labels_in_roi = lsif.GetLabels()
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# if n in labels_in_roi:
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# roi_hu = sitk.GetArrayFromImage(roi2)
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# threshold = np.percentile(roi_hu, 60)
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# else:
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# threshold = lsif.GetMedian(labels_in_roi[0])
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stats = sitk.LabelStatisticsImageFilter()
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stats.UseHistogramsOn() # Required for median calculation
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stats.Execute(roi, binary)
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# Get the median for the region where mask == 1
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threshold = stats.GetMedian(1)
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cortical = sitk.BinaryThreshold(roi2, lowerThreshold=threshold, upperThreshold=10000, outsideValue=0, insideValue=1)
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cortical_path = os.path.join(output_base, f"{label_name}_cortical.nii.gz")
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sitk.WriteImage(cortical, cortical_path)
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return roi_path, binary_path, roi2_path, cortical_path
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"""
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Dataset/
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└── standardized/
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└── subject001/
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├── L1_roi.nii.gz
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├── L1_binary.nii.gz
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├── L1_roi2.nii.gz
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├── L1_cortical.nii.gz
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├── L2_roi.nii.gz
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├── L2_binary.nii.gz
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...
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""" |