Automatic segmentation of kidney and kidney tumors using the cascaded dense network combined with cLSTM in CT scan

Authors
Kim, Jae-Hun
Yang, Ehwa
Kyo Kim, Chan

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
In this study, we develop the cascaded deep neural network model for automatic segmentation of the kidney and kidney tumors in CT scans. We used the fully dense network (to extract inner-slice image features) combined with bi-cLSTM (to extract inter-slice image features) for segmentation of kidney and kidney tumors. The whole CT scan is preprocessing for resizing (256*256) and intensity normalization (clipping between -600 and 400 and then normalizing between 0 and 1), and then entered into 1 st neural network (growth factor = 8, and the consecutive slices = 3) for segmentation of kidney. And the output of 1 st network is resized into 96*96, and entered into 2 nd neural network, which is the same architecture of 1 st network excepting growth factor = 16, and the consecutive slices = 5. Our cascaded deep neural network showed the dice scores of 0.932 (train), and 0.884 (validation) for segmentation of kidney and the dice scores of 0.845 (train), and 0.696 (validation) for segmentation of kidney tumors.

Identifiers
doi: 10.24926/548719.041