496 lines
18 KiB
Python
496 lines
18 KiB
Python
# import nibabel as nib
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# import matplotlib.pyplot as plt
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# import numpy as np
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# import os
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# import SimpleITK as sitk
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# def load_nifti(file_path):
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# """載入NIfTI檔案"""
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# return nib.load(file_path).get_fdata()
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# def process_eyes_segmentation(pred_array):
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# """
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# 後處理眼睛的分割結果
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# Parameters:
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# pred_array: numpy array, 預測的分割結果
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# Returns:
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# result: numpy array, 處理後的分割結果
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# """
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# # 轉換為SimpleITK影像以使用形態學操作
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# pred_img = sitk.GetImageFromArray(pred_array)
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# # 提取左右眼的mask (標籤2和3)
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# right_eye = (pred_array == 2).astype(np.uint8)
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# left_eye = (pred_array == 3).astype(np.uint8)
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# # 合併左右眼
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# combined_eyes = right_eye | left_eye
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# combined_eyes_sitk = sitk.GetImageFromArray(combined_eyes)
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# # 應用形態學操作
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# # 1. 閉運算填充小洞
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# closing_filter = sitk.BinaryMorphologicalClosingImageFilter()
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# closing_filter.SetKernelRadius(2)
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# closed_eyes = closing_filter.Execute(combined_eyes_sitk)
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# # 2. 移除小的孤立區域
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# cc_filter = sitk.ConnectedComponentImageFilter()
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# cc_filter.FullyConnectedOn()
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# components = cc_filter.Execute(closed_eyes)
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# # 獲取標籤統計信息
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# label_stats = sitk.LabelShapeStatisticsImageFilter()
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# label_stats.Execute(components)
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# # 保留最大的兩個連通區域（左右眼）
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# n_objects = cc_filter.GetObjectCount()
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# if n_objects > 2:
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# sizes = [(i, label_stats.GetPhysicalSize(i)) for i in range(1, n_objects + 1)]
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# sizes.sort(key=lambda x: x[1], reverse=True)
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# # 創建mask只保留最大的兩個區域
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# binary_mask = sitk.Image(components.GetSize(), sitk.sitkUInt8)
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# binary_mask.CopyInformation(components)
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# binary_mask = sitk.Mask(components, components)
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# for i in range(n_objects):
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# if i >= 2: # 移除小於第二大的區域
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# binary_mask = sitk.BinaryThreshold(binary_mask,
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# sizes[i][0], sizes[i][0],
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# 0, 1)
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# else:
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# binary_mask = closed_eyes
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# # 將處理後的mask轉回numpy array
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# processed_mask = sitk.GetArrayFromImage(binary_mask)
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# # 基於質心分離左右眼
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# label_img = sitk.GetArrayFromImage(components)
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# regions = []
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# for i in range(1, n_objects + 1):
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# if label_stats.GetPhysicalSize(i) > 0:
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# region_mask = (label_img == i)
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# centroid = np.mean(np.where(region_mask), axis=1)
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# regions.append((i, centroid[2])) # 使用x座標來判斷左右
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# # 根據質心x座標排序
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# regions.sort(key=lambda x: x[1])
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# # 重建左右眼標籤
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# result = np.zeros_like(pred_array)
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# if len(regions) >= 2:
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# # 將最右邊的區域標記為右眼(標籤2)
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# result[label_img == regions[-1][0]] = 2
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# # 將最左邊的區域標記為左眼(標籤3)
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# result[label_img == regions[0][0]] = 3
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# return result
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# def process_optic_nerves_segmentation(pred_array):
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# """
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# 後處理視神經的分割結果
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# Parameters:
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# pred_array: numpy array, 預測的分割結果
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# Returns:
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# result: numpy array, 處理後的分割結果
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# """
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# # 提取視神經的mask (標籤5和6)
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# right_nerve = (pred_array == 5).astype(np.uint8)
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# left_nerve = (pred_array == 6).astype(np.uint8)
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# # 合併視神經
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# combined_nerves = right_nerve | left_nerve
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# combined_nerves_sitk = sitk.GetImageFromArray(combined_nerves)
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# # 應用形態學操作
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# closing_filter = sitk.BinaryMorphologicalClosingImageFilter()
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# closing_filter.SetKernelRadius(2)
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# closed_nerves = closing_filter.Execute(combined_nerves_sitk)
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# # 連通區域分析
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# cc_filter = sitk.ConnectedComponentImageFilter()
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# cc_filter.FullyConnectedOn()
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# components = cc_filter.Execute(closed_nerves)
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# # 獲取標籤統計信息
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# label_stats = sitk.LabelShapeStatisticsImageFilter()
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# label_stats.Execute(components)
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# # 處理連通區域
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# n_objects = cc_filter.GetObjectCount()
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# label_img = sitk.GetArrayFromImage(components)
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# regions = []
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# for i in range(1, n_objects + 1):
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# if label_stats.GetPhysicalSize(i) > 0:
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# region_mask = (label_img == i)
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# centroid = np.mean(np.where(region_mask), axis=1)
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# regions.append((i, centroid[2]))
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# # 根據質心x座標排序
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# regions.sort(key=lambda x: x[1])
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# # 重建左右視神經標籤
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# result = np.zeros_like(pred_array)
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# if len(regions) >= 2:
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# # 將最右邊的區域標記為右視神經(標籤5)
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# result[label_img == regions[-1][0]] = 5
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# # 將最左邊的區域標記為左視神經(標籤6)
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# result[label_img == regions[0][0]] = 6
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# return result
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# def post_process_symmetrical_organs(prediction):
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# """對稱器官後處理"""
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# processed_prediction = prediction.copy()
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# # 處理眼睛
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# eyes_result = process_eyes_segmentation(processed_prediction)
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# # 更新眼睛的標籤
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# processed_prediction[eyes_result > 0] = eyes_result[eyes_result > 0]
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# # 處理視神經
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# nerves_result = process_optic_nerves_segmentation(processed_prediction)
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# # 更新視神經的標籤
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# processed_prediction[nerves_result > 0] = nerves_result[nerves_result > 0]
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# return processed_prediction
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# def create_colored_mask(data):
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# """創建彩色遮罩"""
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# color_dict = {
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# 1: [144/255, 238/255, 144/255], # Brainstem - 綠色
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# 2: [255/255, 218/255, 150/255], # Right_Eye - 淺黃色
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# 3: [205/255, 170/255, 125/255], # Left_Eye - 棕色
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# 4: [135/255, 206/255, 235/255], # Optic_Chiasm - 藍色
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# 5: [255/255, 99/255, 71/255], # Right_Optic_Nerve - 亮珊瑚紅
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# 6: [255/255, 160/255, 122/255], # Left_Optic_Nerve - 淺珊瑚紅
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# 7: [255/255, 0/255, 0/255] # TV - 亮紅色
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# }
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# # 創建一個具有透明背景的遮罩
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# mask = np.zeros((*data.shape, 4)) # 使用4通道（RGBA）
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# for label, color in color_dict.items():
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# # 為每個器官設置顏色，包括完全不透明的 alpha 通道
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# organ_mask = (data == label)
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# if np.any(organ_mask): # 只處理存在的器官
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# mask[organ_mask] = [*color, 1.0] # RGB + alpha
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# return mask
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# def get_brain_bounds(image):
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# """獲取腦部區域的邊界"""
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# mask = image > np.percentile(image, 1)
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# rows = np.any(mask, axis=1)
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# cols = np.any(mask, axis=0)
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# rmin, rmax = np.where(rows)[0][[0, -1]]
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# cmin, cmax = np.where(cols)[0][[0, -1]]
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# center_r = (rmin + rmax) // 2
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# center_c = (cmin + cmax) // 2
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# radius = int(max(rmax - rmin, cmax - cmin) * 0.55)
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# margin = int(radius * 0.15)
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# rmin = max(center_r - radius - margin, 0)
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# rmax = min(center_r + radius + margin, image.shape[0])
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# cmin = max(center_c - radius - margin, 0)
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# cmax = min(center_c + radius + margin, image.shape[1])
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# return rmin, rmax, cmin, cmax
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# def check_organs_in_slice(slice_data):
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# """檢查切片中存在的器官標籤"""
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# unique_labels = set(np.unique(slice_data))
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# if 0 in unique_labels:
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# unique_labels.remove(0)
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# organ_dict = {
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# 1: "Brainstem",
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# 2: "Right_Eye",
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# 3: "Left_Eye",
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# 4: "Optic_Chiasm",
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# 5: "Right_Optic_Nerve",
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# 6: "Left_Optic_Nerve",
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# 7: "TV"
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# }
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# return {organ_dict[label] for label in unique_labels if label in organ_dict}
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# def save_slice(image, ground_truth, prediction, slice_num, save_path):
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# """保存指定切片的比較圖"""
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# plt.clf()
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# fig, axes = plt.subplots(1, 2, figsize=(25, 11))
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# plt.subplots_adjust(wspace=0.01)
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# img_slice = np.rot90(image[:, :, slice_num])
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# rmin, rmax, cmin, cmax = get_brain_bounds(img_slice)
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# # 調整圖像對比度以提高可見度
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# p2, p98 = np.percentile(img_slice, (2, 98))
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# img_slice = np.clip(img_slice, p2, p98)
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# img_slice = (img_slice - p2) / (p98 - p2)
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# # Ground Truth
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# gt_slice = np.rot90(ground_truth[:, :, slice_num])
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# gt_mask = create_colored_mask(gt_slice)
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# # 使用 imshow 的 zorder 參數來控制圖層順序
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# axes[0].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
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# axes[0].imshow(gt_mask[rmin:rmax, cmin:cmax], zorder=2)
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# axes[0].set_title('Ground Truth', fontsize=32, pad=20, weight='bold')
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# axes[0].axis('off')
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# # 對整個prediction進行後處理
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# processed_prediction = post_process_symmetrical_organs(prediction)
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# # Prediction
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# pred_slice = np.rot90(processed_prediction[:, :, slice_num])
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# pred_mask = create_colored_mask(pred_slice)
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# axes[1].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
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# axes[1].imshow(pred_mask[rmin:rmax, cmin:cmax], zorder=2)
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# axes[1].set_title('Prediction', fontsize=32, pad=20, weight='bold')
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# axes[1].axis('off')
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# plt.savefig(save_path, bbox_inches='tight', dpi=300, pad_inches=0.05)
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# plt.close()
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# print(f"已保存切片 {slice_num} 至: {save_path}")
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# if __name__ == "__main__":
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# base_dir = "/mnt/1248/onlylian"
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# image_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset012_OAR_TV/imagesTs/OAR_TV_092_0000.nii.gz")
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# gt_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset012_OAR_TV/labelsTs/OAR_TV_092.nii.gz")
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# pred_path = os.path.join(base_dir, "T1C_OAR_TV/output_predictions/2d/fold_2/OAR_TV_092.nii.gz")
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# save_dir = os.path.join(base_dir, "T1C_OAR_TV")
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# os.makedirs(save_dir, exist_ok=True)
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# print("正在載入影像...")
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# image = load_nifti(image_path)
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# ground_truth = load_nifti(gt_path)
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# prediction = load_nifti(pred_path)
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# print("影像載入完成")
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# print("\n分析切片中的器官...")
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# total_slices = image.shape[2]
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# for slice_num in range(total_slices):
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# gt_slice = ground_truth[:, :, slice_num]
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# pred_slice = prediction[:, :, slice_num]
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# gt_organs = check_organs_in_slice(gt_slice)
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# pred_organs = check_organs_in_slice(pred_slice)
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# if gt_organs or pred_organs:
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# print(f"\n切片 {slice_num}:")
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# print(f"Ground Truth 包含: {', '.join(sorted(gt_organs))}")
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# print(f"Prediction 包含: {', '.join(sorted(pred_organs))}")
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# print("\n開始切片選擇和保存過程...")
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# while True:
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# try:
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# slice_num = int(input("\n請輸入要保存的切片編號 (-1 退出): "))
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# if slice_num == -1:
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# print("程序結束")
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# break
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# if 0 <= slice_num < total_slices:
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# save_path = os.path.join(save_dir, f'comparison_OAR_TV_092_{slice_num:03d}.png')
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# save_slice(image, ground_truth, prediction, slice_num, save_path)
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# else:
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# print(f"切片編號必須在 0 到 {total_slices-1} 之間")
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# except ValueError:
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# print("請輸入有效的數字")
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# except Exception as e:
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# print(f"發生錯誤: {str(e)}")
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import nibabel as nib
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import matplotlib.patches as patches
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def load_nifti(file_path):
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"""載入NIfTI檔案"""
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return nib.load(file_path).get_fdata()
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def create_colored_mask(data):
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"""創建彩色遮罩"""
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color_dict = {
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1: [144/255, 238/255, 144/255], # Brainstem - 綠色
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2: [255/255, 218/255, 150/255], # Right_Eye - 淺黃色
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3: [205/255, 170/255, 125/255], # Left_Eye - 棕色
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4: [135/255, 206/255, 235/255], # Optic_Chiasm - 藍色
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5: [255/255, 99/255, 71/255], # Right_Optic_Nerve - 亮珊瑚紅
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6: [255/255, 160/255, 122/255], # Left_Optic_Nerve - 淺珊瑚紅
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7: [255/255, 0/255, 0/255] # TV - 亮紅色
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}
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mask = np.zeros((*data.shape, 4))
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for label, color in color_dict.items():
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organ_mask = (data == label)
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if np.any(organ_mask):
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mask[organ_mask] = [*color, 1.0]
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return mask
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def get_brain_bounds(image):
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"""獲取腦部區域的邊界"""
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mask = image > np.percentile(image, 1)
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rows = np.any(mask, axis=1)
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cols = np.any(mask, axis=0)
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rmin, rmax = np.where(rows)[0][[0, -1]]
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cmin, cmax = np.where(cols)[0][[0, -1]]
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center_r = (rmin + rmax) // 2
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center_c = (cmin + cmax) // 2
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radius = int(max(rmax - rmin, cmax - cmin) * 0.55)
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margin = int(radius * 0.15)
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return (
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max(center_r - radius - margin, 0),
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min(center_r + radius + margin, image.shape[0]),
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max(center_c - radius - margin, 0),
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min(center_c + radius + margin, image.shape[1])
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)
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def check_organs_in_slice(slice_data):
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"""檢查切片中存在的器官標籤"""
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unique_labels = set(np.unique(slice_data))
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if 0 in unique_labels:
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unique_labels.remove(0)
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organ_dict = {
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1: "Brainstem",
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2: "Right_Eye",
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3: "Left_Eye",
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4: "Optic_Chiasm",
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5: "Right_Optic_Nerve",
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6: "Left_Optic_Nerve",
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7: "TV"
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}
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return {organ_dict[label] for label in unique_labels if label in organ_dict}
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def create_circular_mask(shape, center, radius):
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"""創建圓形遮罩"""
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Y, X = np.ogrid[:shape[0], :shape[1]]
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dist_from_center = np.sqrt((X - center[0])**2 + (Y - center[1])**2)
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mask = dist_from_center <= radius
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return mask
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def save_slice(image, ground_truth, prediction, slice_num, save_path):
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"""保存指定切片的比較圖"""
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plt.clf()
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fig, axes = plt.subplots(1, 3, figsize=(36, 11))
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plt.subplots_adjust(wspace=0.01)
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img_slice = np.rot90(image[:, :, slice_num])
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rmin, rmax, cmin, cmax = get_brain_bounds(img_slice)
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# 計算圓形遮罩的參數
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height, width = rmax-rmin, cmax-cmin
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center = (width//2, height//2)
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radius = min(width, height)//2
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mask = create_circular_mask((height, width), center, radius)
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# 調整圖像對比度
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p2, p98 = np.percentile(img_slice, (2, 98))
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img_slice = np.clip(img_slice, p2, p98)
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img_slice = (img_slice - p2) / (p98 - p2)
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cropped_slice = img_slice[rmin:rmax, cmin:cmax]
|
||
gt_slice = np.rot90(ground_truth[:, :, slice_num])[rmin:rmax, cmin:cmax]
|
||
pred_slice = np.rot90(prediction[:, :, slice_num])[rmin:rmax, cmin:cmax]
|
||
|
||
# 創建遮罩
|
||
gt_mask = create_colored_mask(gt_slice)
|
||
pred_mask = create_colored_mask(pred_slice)
|
||
|
||
# 設置背景顏色為黑色
|
||
fig.patch.set_facecolor('black')
|
||
|
||
titles = ['Original Image', 'Ground Truth', 'Prediction']
|
||
images = [
|
||
(cropped_slice, None),
|
||
(cropped_slice, gt_mask),
|
||
(cropped_slice, pred_mask)
|
||
]
|
||
|
||
for ax, title, (img, overlay) in zip(axes, titles, images):
|
||
ax.set_facecolor('black')
|
||
|
||
# 應用圓形遮罩
|
||
masked_img = np.copy(img)
|
||
masked_img[~mask] = 0
|
||
|
||
# 顯示基礎圖像
|
||
ax.imshow(masked_img, cmap='gray', zorder=1)
|
||
|
||
# 如果有overlay，顯示它
|
||
if overlay is not None:
|
||
masked_overlay = overlay.copy()
|
||
masked_overlay[~mask] = [0, 0, 0, 0]
|
||
ax.imshow(masked_overlay, zorder=2)
|
||
|
||
# 設置標題
|
||
ax.set_title(title, fontsize=32, pad=20, weight='bold', color='white')
|
||
ax.axis('off')
|
||
|
||
plt.savefig(save_path, bbox_inches='tight', dpi=300, pad_inches=0.05,
|
||
facecolor='black', edgecolor='none')
|
||
plt.close()
|
||
print(f"已保存切片 {slice_num} 至: {save_path}")
|
||
|
||
if __name__ == "__main__":
|
||
base_dir = "/mnt/1248/onlylian"
|
||
image_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset012_OAR_TV/imagesTs/OAR_TV_092_0000.nii.gz")
|
||
gt_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset012_OAR_TV/labelsTs/OAR_TV_092.nii.gz")
|
||
pred_path = os.path.join(base_dir, "T1C_OAR_TV/output_predictions/2d/fold_2/OAR_TV_092.nii.gz")
|
||
save_dir = os.path.join(base_dir, "T1C_OAR_TV")
|
||
|
||
os.makedirs(save_dir, exist_ok=True)
|
||
|
||
print("正在載入影像...")
|
||
image = load_nifti(image_path)
|
||
ground_truth = load_nifti(gt_path)
|
||
prediction = load_nifti(pred_path)
|
||
print("影像載入完成")
|
||
|
||
print("\n分析切片中的器官...")
|
||
total_slices = image.shape[2]
|
||
for slice_num in range(total_slices):
|
||
gt_slice = ground_truth[:, :, slice_num]
|
||
pred_slice = prediction[:, :, slice_num]
|
||
|
||
gt_organs = check_organs_in_slice(gt_slice)
|
||
pred_organs = check_organs_in_slice(pred_slice)
|
||
|
||
if gt_organs or pred_organs:
|
||
print(f"\n切片 {slice_num}:")
|
||
print(f"Ground Truth 包含: {', '.join(sorted(gt_organs))}")
|
||
print(f"Prediction 包含: {', '.join(sorted(pred_organs))}")
|
||
|
||
print("\n開始切片選擇和保存過程...")
|
||
while True:
|
||
try:
|
||
slice_num = int(input("\n請輸入要保存的切片編號 (-1 退出): "))
|
||
if slice_num == -1:
|
||
print("程序結束")
|
||
break
|
||
if 0 <= slice_num < total_slices:
|
||
save_path = os.path.join(save_dir, f'comparison_OAR_TV_092_{slice_num:03d}.png')
|
||
save_slice(image, ground_truth, prediction, slice_num, save_path)
|
||
else:
|
||
print(f"切片編號必須在 0 到 {total_slices-1} 之間")
|
||
except ValueError:
|
||
print("請輸入有效的數字")
|
||
except Exception as e:
|
||
print(f"發生錯誤: {str(e)}")
|
||
|
||
|
||
|
||
|
||
|