Automatic Kidney and Tumor Segmentation with Attention-based V-Net

Authors
Hu, Yucheng
Deng, Han
Zhou, Yang
Chen, Yimin
Hao, Zhou
Yang, Wanqi

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Deep learning, especially Convolutional Neural Networks (CNNs) have been implemented to resolve a variety of both computer vision and medical image analysis problems recently. Among a rather wide range of Segmentation CNNs, V-Net is a relatively popular one, which is also an extended version of U-Net which processes 2D images. In this work, we propose an innovative V-Net with a embeded attention module. Inspired by spatial neural attention for generating pseudo-annotations, we modify the Decoupled attention into 3D version and insert it into the V-Net. This CNN network is trained end-to-end on CT volumes, and able to learn to predict segmentation blocks for a certain case. The definition of “block” will be elaborated in Section 3. Finally, the blocks will be concatenated to create a complete segmentation for a single case.

Identifiers
doi: 10.24926/548719.087