2019 Kidney Tumor Segmentation Challenge Method Manuscript

Authors
Jiao, MengLei
Liu, Hong

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
This paper framework in detail for KiTS19, which is the 2019 Kidney Tumor Segmentation Challenge. We adopt two model ResUNetSM and DeepLabV3 plus to segment kidney and tumor respectively. Firstly, we propose a model ResUNetSM to segment kidney, which uses ResNet for encoder, and adopts SELayer and MobileBlock for decoder. ResUNetSM also adopts ASPP and skip-connect structure. To segment tumor region, we adopt DeepLabV3 plus and segment tumor in the 3D ROI region from above kidney segmentation results to reduce noise. Finally, we use 3DCRF and 3D connected component analysis as post-processing to improve the final segmentation results. Our framework gets the 96.31% mean dice for kidney and 81.64% mean dice for tumor on validation set.

Identifiers
doi: 10.24926/548719.042