LIENWEIYING/CT_CTC_T1_OAR/chiasm.py

import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
import os

def load_nifti(file_path):
    """載入NIfTI檔案"""
    return nib.load(file_path).get_fdata()

def create_colored_mask(data):
    """創建彩色遮罩"""
    color_dict = {
        1: [144/255, 238/255, 144/255],  # Brainstem - 綠色
        2: [255/255, 218/255, 150/255],  # Right_Eye - 淺黃色
        3: [205/255, 170/255, 125/255],  # Left_Eye - 棕色
        4: [135/255, 206/255, 235/255],  # Optic_Chiasm - 藍色
        5: [255/255, 99/255, 71/255],    # Right_Optic_Nerve - 亮珊瑚紅
        6: [255/255, 160/255, 122/255]   # Left_Optic_Nerve - 淺珊瑚紅
    }
    
    # 創建一個具有透明背景的遮罩
    mask = np.zeros((*data.shape, 4))  # 使用4通道（RGBA）
    
    for label, color in color_dict.items():
        # 為每個器官設置顏色，包括完全不透明的 alpha 通道
        organ_mask = (data == label)
        if np.any(organ_mask):  # 只處理存在的器官
            mask[organ_mask] = [*color, 1.0]  # RGB + alpha
    
    return mask

def get_brain_bounds(image):
    """獲取腦部區域的邊界"""
    mask = image > np.percentile(image, 1)
    rows = np.any(mask, axis=1)
    cols = np.any(mask, axis=0)
    rmin, rmax = np.where(rows)[0][[0, -1]]
    cmin, cmax = np.where(cols)[0][[0, -1]]
    
    center_r = (rmin + rmax) // 2
    center_c = (cmin + cmax) // 2
    
    radius = int(max(rmax - rmin, cmax - cmin) * 0.55)
    margin = int(radius * 0.15)
    
    rmin = max(center_r - radius - margin, 0)
    rmax = min(center_r + radius + margin, image.shape[0])
    cmin = max(center_c - radius - margin, 0)
    cmax = min(center_c + radius + margin, image.shape[1])
    
    return rmin, rmax, cmin, cmax

def check_organs_in_slice(slice_data):
    """檢查切片中存在的器官標籤"""
    unique_labels = set(np.unique(slice_data))
    if 0 in unique_labels:
        unique_labels.remove(0)
    organ_dict = {
        1: "Brainstem",
        2: "Right_Eye",
        3: "Left_Eye",
        4: "Optic_Chiasm",
        5: "Right_Optic_Nerve",
        6: "Left_Optic_Nerve"
    }
    return {organ_dict[label] for label in unique_labels if label in organ_dict}

def save_slice(image_ct, image_mri, ground_truth, prediction, slice_num, save_path):
    """保存指定切片的比較圖"""
    plt.clf()
    fig, axes = plt.subplots(1, 2, figsize=(25, 11))
    plt.subplots_adjust(wspace=0.01)
    
    # Process CT and MRI images
    img_slice_ct = np.rot90(image_ct[:, :, slice_num])
    img_slice_mri = np.rot90(image_mri[:, :, slice_num])
    
    # Get bounds from CT image
    rmin, rmax, cmin, cmax = get_brain_bounds(img_slice_ct)
    
    # 調整CT和MRI圖像的對比度
    def adjust_contrast(img):
        p2, p98 = np.percentile(img, (2, 98))
        return np.clip((img - p2) / (p98 - p2), 0, 1)
    
    ct_norm = adjust_contrast(img_slice_ct)
    mri_norm = adjust_contrast(img_slice_mri)
    
    # Blend images with equal weights
    blended_img = 0.5 * ct_norm + 0.5 * mri_norm
    
    # Ground Truth
    gt_slice = np.rot90(ground_truth[:, :, slice_num])
    gt_mask = create_colored_mask(gt_slice)
    
    # 使用 zorder 參數來控制圖層順序
    axes[0].imshow(blended_img[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
    axes[0].imshow(gt_mask[rmin:rmax, cmin:cmax], zorder=2)
    axes[0].set_title('Ground Truth', fontsize=32, pad=20, weight='bold')
    axes[0].axis('off')
    
    # Prediction
    pred_slice = np.rot90(prediction[:, :, slice_num])
    pred_mask = create_colored_mask(pred_slice)
    
    axes[1].imshow(blended_img[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
    axes[1].imshow(pred_mask[rmin:rmax, cmin:cmax], zorder=2)
    axes[1].set_title('Prediction', fontsize=32, pad=20, weight='bold')
    axes[1].axis('off')
    
    plt.savefig(save_path, bbox_inches='tight', dpi=300, pad_inches=0.05)
    plt.close()
    print(f"已保存切片 {slice_num} 至: {save_path}")

if __name__ == "__main__":
    # 載入影像檔案
    image_path_1 = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset221_OAR/imagesTs/OAR_082_0000.nii.gz' #CT
    image_path_2 = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset221_OAR/imagesTs/OAR_082_0001.nii.gz' #MRI
    gt_path = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset221_OAR/labelsTs/OAR_082.nii.gz'
    pred_path = '/mnt/1248/onlylian/CT_CTC_T1_OAR/output_predictions/3d_fullres/fold_4/OAR_082.nii.gz'
    
    # 設定保存路徑
    save_dir = '/mnt/1248/onlylian/CT_CTC_T1_OAR'
    os.makedirs(save_dir, exist_ok=True)

    # 讀取影像
    print("正在載入影像...")
    image_ct = load_nifti(image_path_1)
    image_mri = load_nifti(image_path_2)
    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_ct.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_optic_chiasm_082_{slice_num:03d}.png')
                save_slice(image_ct, image_mri, 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)}")