Kidney and Kidney Tumor Segmentation Using Two- stage Convolutional Neural Network

Authors
Lee, Junghyun
Song, Joonyoung
Yang, Serin
Han, Inhwa
Chul Ye, Jong

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Kidney tumor is typically diagnosed using computed tomography (CT) imaging by investigating geometric features of kidney tumor. For a reliable diagnosis and treatment planning, kidney tumor quantification is necessary. However, manual segmentation by human requires time and expertise. In addition, inter/intra variability of segmentation results can lead to suboptimal decision. In this study, we propose the two-stage segmentation method using 2.5D and 3D convolutional neural network for kidney and kidney tumor delineation. The two stage model was trained with multi-task loss for pixel-wise cross-entropy loss function for segmentation task and mean square error function for regression task. Experimental results confirm that the proposed method effectively segments kidney and kidney tumor.

Identifiers
doi: 10.24926/548719.059