Kidney and tumor segmentation using combined Deep learning method

Authors
Kamkova, Yuliia
Ali Qadir, Hemin
Jakob, Ole
Prasanna Kumar, Rahul

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
This paper presents our method for automatic segmentation for kidney and tumor as part of the Kidney Tumor Segmentation Challenge (KiTS19). The KiTS19 Challenge had released a dataset of 300 unique kidney cancer patients, with manual annotations done by Climb 4 Kidney Cancer (C4KC). Here we have proposed our new combined cascade deep learning (DL) approach for solving the tasks of the challenge. We used deep learning based detection for localising kidney with the tumor, followed by deep learning based segmentation to create the labels for kidney and tumor locally. Our approach resulted in high recall (96.13) and high Jacquard score (95.4) on the randomly selected 30 volumes that were picked as the validation set.

Identifiers
doi: 10.24926/548719.091