Kidney Tumour Segmentation

Authors
Sharma, Rochan

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Medical Image Segmentation is a challenging field in the area of Computer Vision. In this work Two deep learning models were explored namely U-Net and ENet. The reason to shortlist U-Net was it is suitable on a small data set and also originally designed for Biomedical Image segmentation. However when compared to ENet it is much slower. To speed up the process of Kidney Tumor segmentation , ENet was shortlisted and also experimented on the data set provided. ENet was very fast as compared to U-Net , However some visual representations of the predicted results have shown promising results in U-Net better then ENet. A classification model called as Xception Model was also considered right in the first phase , so as to shortlist those slices from the CT which have the presence of Kidney. So that the output obtained can be given as an input to semantic segmentation model. This would allows us to speed up the process of Kidney Tumor Segmentation. Finally performance parameter which was used for evaluation segmentation models was IOU.

Identifiers
doi: 10.24926/548719.080