Two Stream Auto-encoder Decoder Network for Kidney and Tumor Segmentation

Authors
Moradi, Pouria
Azad, Reza
Asadi-Aghbolaghi, Maryam

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
In this competition, we apply two-streams auto-encoder decoder structure for learning kidney and kidneys tumor segmentation. To do so, first, we extract axial layers of the tissues along with their segmentation mask from the 3D volume. These axial layers are then clipped using Hanford distance between +512 to -512 to eliminate non-object of interest. These axial layers are then normalized to form the 2D grayscale images. For each of these normalized images, we generate kidney and kidney tumor masks to train two-stream deep networks. The two-streams deep model learns kidney and tumor masks separately and they generate final mask by concatenating the generated masks. We utilize BCDU-net (extended version of U-Net model) as a deep auto-encoder decoder model for segmentation. We utilize 70% of the Kits19 as the training set and the rest of data as the validation set. Experimental results demonstrate that the proposed structure achieves state-of-the-art performance in the segmentation of kidney and tumor region.

Identifiers
doi: 10.24926/548719.053