Multi-Encoder U-Net for Automatic Kidney Tumor Segmentation

Authors
Chen, Xueying
Xu, Chao

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Kidney tumor segmentation is a difficult yet critical task for medical image analysis. In recent years, deep learning based methods have achieved many excellent performances in the field of medical image segmentation. In this paper, we propose a Multi-Encoder U-Net segmentation method to tackle the challenging problem of kidney tumor segmentation from CT images. Our Multi-Encoder U-Net method uses three different depth networks as encoders for kidney tumor segmentation: VGG16, ResNet34, ResNet50, a feature fusion networkFED-Net is also used simultaneously, finally fusing the four results. We tested our method on the dataset of MICCAI 2019 Kidney Tumor Segmentation Challenge(KiTS).

Identifiers
doi: 10.24926/548719.024