病理图像（WSI）处理

最近在做肺腺癌分型相关的项目，记录一下对病理图像（WSI）处理的一些方式。

滑动窗口切片

def crop_img(ori_img, mask_img, cropsize, overlap, image_filename_, label_dataset_path, unlabel_dataset_path):
    """
    裁剪图像为指定格式并保存成tiff
    输入为array形式的数组
    """
    num = 0
    mask_threshold = 300
    height = ori_img.shape[0]
    width = ori_img.shape[1]
    print(height)
    print(width)

    # 从左上开始裁剪
    for i in range(int(height / (cropsize * (1 - overlap)))):  # 行裁剪次数
        for j in range(int(width / (cropsize * (1 - overlap)))):  # 列裁剪次数
            ori_cropped = ori_img[
                          int(i * cropsize * (1 - overlap)): int(i * cropsize * (1 - overlap) + cropsize),
                          int(j * cropsize * (1 - overlap)): int(j * cropsize * (1 - overlap) + cropsize),
                          :,
                          ]  # max函数是为了防止i，j为0时索引为负数 # 通道不裁剪
            mask_cropped = mask_img[
                           int(i * cropsize * (1 - overlap)): int(i * cropsize * (1 - overlap) + cropsize),
                           int(j * cropsize * (1 - overlap)): int(j * cropsize * (1 - overlap) + cropsize),
                           ]  # max函数是为了防止i，j为0时索引为负数 # 通道不裁剪

            if np.sum(mask_cropped) < mask_threshold:
                ori_path = os.path.join(unlabel_dataset_path,
                                        image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
                mask_path = os.path.join(unlabel_dataset_path,
                                         image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')
            else:
                ori_path = os.path.join(label_dataset_path,
                                        image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
                mask_path = os.path.join(label_dataset_path,
                                         image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')
            print(ori_path)
            cv2.imwrite(ori_path, ori_cropped)
            cv2.imwrite(mask_path, mask_cropped)
            num = num + 1

    #  向前裁剪最后的列
    for i in range(int(height / (cropsize * (1 - overlap)))):
        ori_cropped = ori_img[
                      int(i * cropsize * (1 - overlap)): int(i * cropsize * (1 - overlap) + cropsize),
                      int(j * cropsize * (1 - overlap)): int(j * cropsize * (1 - overlap) + cropsize),
                      :,
                      ]
        mask_cropped = mask_img[
                       int(i * cropsize * (1 - overlap)): int(i * cropsize * (1 - overlap) + cropsize),  # 所有行
                       width - cropsize: width,  # 最后256列
                       ]

        if np.sum(mask_cropped) < mask_threshold:
            ori_path = os.path.join(unlabel_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
            mask_path = os.path.join(unlabel_dataset_path,
                                     image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')
        else:
            ori_path = os.path.join(label_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
            mask_path = os.path.join(label_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')
        print(ori_path)
        cv2.imwrite(ori_path, ori_cropped)
        cv2.imwrite(mask_path, mask_cropped)
        num = num + 1

    # 向前裁剪最后的行
    for j in range(int(width / (cropsize * (1 - overlap)))):
        ori_cropped = ori_img[
                      int(i * cropsize * (1 - overlap)): int(i * cropsize * (1 - overlap) + cropsize),
                      int(j * cropsize * (1 - overlap)): int(j * cropsize * (1 - overlap) + cropsize),
                      :,
                      ]
        mask_cropped = mask_img[
                       height - cropsize: height,  # 最后256行
                       int(j * cropsize * (1 - overlap)): int(j * cropsize * (1 - overlap) + cropsize),  # 所有列
                       ]
        if np.sum(mask_cropped) < mask_threshold:
            ori_path = os.path.join(unlabel_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
            mask_path = os.path.join(unlabel_dataset_path,
                                     image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')
        else:
            ori_path = os.path.join(label_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
            mask_path = os.path.join(label_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')
        print(ori_path)
        cv2.imwrite(ori_path, ori_cropped)
        cv2.imwrite(mask_path, mask_cropped)
        num = num + 1

    # 裁剪右下角
    ori_cropped = ori_img[
                  height - cropsize: height,
                  width - cropsize: width,
                  :,  # 通道不裁剪
                  ]
    mask_cropped = mask_img[
                   height - cropsize: height,
                   width - cropsize: width,
                   ]
    if np.sum(mask_cropped) < mask_threshold:
        ori_path = os.path.join(unlabel_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
        mask_path = os.path.join(unlabel_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')
    else:
        ori_path = os.path.join(label_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.jpg')
        mask_path = os.path.join(label_dataset_path, image_filename_.split('/')[-1][:-4] + '_' + str(num) + '.png')

    print(ori_path)
    cv2.imwrite(ori_path, ori_cropped)
    cv2.imwrite(mask_path, mask_cropped)