Renal cancer is one of the 10 most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) is an effective way to treat renal cancer. Localization and delineation of the renal tumor from pre-operative CT Angiography (CTA) is an important step for LPN surgery planning. Recently, with the development of the technique of deep learning, deep neural networks can be trained to provide accurate pixel-wise renal tumor segmentation in CTA images. However, constructing the training dataset with a large amount of pixel-wise annotations is a time-consuming task for the radiologists. Therefore, weakly-supervised approaches attract more interest in research.
In this paper, we proposed a novel weakly-supervised convolutional neural network (CNN) for renal tumor segmentation. A three-stage framework was introduced to train the CNN with the weak annotations of renal tumors, i.e. the bounding boxes of renal tumors. The framework includes pseudo masks generation, group and weighted training phases. Clinical abdominal CT angiographic images of 200 patients were applied to perform the evaluation.
Extensive experimental results show that the proposed method achieves a higher dice coefficient (DSC) of 0.826 than the other two existing weakly-supervised deep neural networks. Furthermore, the segmentation performance is close to the fully supervised deep CNN.
The proposed strategy improves not only the efficiency of network training but also the precision of the segmentation.
BMC medical imaging. 2020 Apr 15*** epublish ***
Guanyu Yang, Chuanxia Wang, Jian Yang, Yang Chen, Lijun Tang, Pengfei Shao, Jean-Louis Dillenseger, Huazhong Shu, Limin Luo
LIST, Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China. ., LIST, Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China., Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China., Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China., Centre de Recherche en Information Biomédicale Sino-Français (CRIBs), Rennes, France.