165 lines
6 KiB
Python
165 lines
6 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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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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}
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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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}
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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_ct, image_mri, 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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# Process CT and MRI images
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img_slice_ct = np.rot90(image_ct[:, :, slice_num])
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img_slice_mri = np.rot90(image_mri[:, :, slice_num])
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# Get bounds from CT image
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rmin, rmax, cmin, cmax = get_brain_bounds(img_slice_ct)
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# 調整CT和MRI圖像的對比度
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def adjust_contrast(img):
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p2, p98 = np.percentile(img, (2, 98))
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return np.clip((img - p2) / (p98 - p2), 0, 1)
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ct_norm = adjust_contrast(img_slice_ct)
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mri_norm = adjust_contrast(img_slice_mri)
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# Blend images with equal weights
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blended_img = 0.5 * ct_norm + 0.5 * mri_norm
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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 zorder 參數來控制圖層順序
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axes[0].imshow(blended_img[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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# Prediction
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pred_slice = np.rot90(prediction[:, :, slice_num])
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pred_mask = create_colored_mask(pred_slice)
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axes[1].imshow(blended_img[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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# 載入影像檔案
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image_path_1 = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset221_OAR/imagesTs/OAR_082_0000.nii.gz' #CT
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image_path_2 = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset221_OAR/imagesTs/OAR_082_0001.nii.gz' #MRI
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gt_path = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset221_OAR/labelsTs/OAR_082.nii.gz'
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pred_path = '/mnt/1248/onlylian/CT_CTC_T1_OAR/output_predictions/3d_fullres/fold_4/OAR_082.nii.gz'
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# 設定保存路徑
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save_dir = '/mnt/1248/onlylian/CT_CTC_T1_OAR'
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os.makedirs(save_dir, exist_ok=True)
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# 讀取影像
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print("正在載入影像...")
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image_ct = load_nifti(image_path_1)
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image_mri = load_nifti(image_path_2)
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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_ct.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_optic_chiasm_082_{slice_num:03d}.png')
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save_slice(image_ct, image_mri, 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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