关键词: 深度学习, 医学影像分割, 目标检测, 级联网络

Abstract:

Evaluation of liver tumors is an important step in the clinical diagnosis and treatment of liver metastases from colorectal cancer. In order to complete the automatic segmentation and detection of liver tumors in abdominal CT images, an improved cascaded deep learning network is proposed. The cascaded network uses U-Net and Mask R-CNN models to perform segmentation and detection tasks, respectively. First, the U-Net model is trained as the first layer of the cascaded network to segment the liver organ as the Region of Interest（ROI）. Then, the morphological activity contour extraction is performed for the ROI region. Finally, the U-Net model and Mask R-CNN are used as the second layer of the cascaded network, the task of accurately segmenting and detecting liver tumors in the ROI is completed, respectively. The experimental results show that the average Dice coefficient of liver metastasis segmentation for the cascade U-Nets model is 74%, the Dice coefficient of the tumor instance segmentation of Mask R-CNN is 67% （confidence is 95%）, and the mean Average Precision（mAP） is 88%.

Key words: deep learning, medical image segmentation, object detection;cascaded network