Segmentation of Kidney and Renal Tumor in CT Scans Using Convolutional Networks

Authors
Yuan, Shaofeng
Yang, Feng
Tang, Yujiao
Xing, Yanyan
Zhang, Liyun

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Accurate segmentation of kidney and renal tumor in CT images is a prerequisite step in surgery planning. However, this task remains a challenge. In this report, we use convolutional networks (ConvNet) to automatically segment kidney and renal tumor. Specifically, we adopt a 2D ConvNet to select a range of slices to be segmented in the inference phase for accelerating segmentation, while a 3D ConvNet is trained to segment regions of interest in the above narrow range. In localization phase, CT images from several publicly available datasets were used for learning localizer. This localizer aims to filter out slices impossible containing kidney and renal tumor, and it was fine-tuned from AlexNet pre-trained on ImageNet. In segmentation phase, a simple U-net with large patch size (160×160×80) was trained to delineate contours of kidney and renal tumor. In the 2019 MICCAI Kidney Tumor Segmentation (KiTS19) Challenge, 5-fold cross-validation was performed on the training set. 168 (80%) CT scans were used for training and remaining 42 (20%) cases were used for validation. The resulting average Dice similarity coefficients are 0.9662 and 0.7905 for kidney and renal tumor, respectively.

Identifiers
doi: 10.24926/548719.012