Segmentation of Kidney and Tumor using Auxiliary Information

Authors
Usman Akbar, Muhammad
Murino, Vittorio
Sona, Diego

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Automatic segmentation of organs and tumors is a prerequisite of many clinical application in radiology. The anatomical variability of organs in the abdomen and especially of tumors makes it difficult for many methods to obtain good segmentations. in this report we present a cascade of two convolutional neural networks allowing to segment an organ followed by the segmentation of a tumor. The advantage of the proposed pipeline is that the preliminary organ segmentation, which is a simpler task, helps the further segmentation of the tumor. The proposed system was evaluated using the KiTS19 challange dataset.

Identifiers
doi: 10.24926/548719.075