Kidney and Tumor Segmentation Based on 3D Context Extracting

Authors
Yan, Bin

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Organ segmentation and lesion detection play a vital role in the computer-aided diagnosis (CAD) systems. The task of this Kits challenge is about kidney and tumor segmentation. We proposed an effective model to complete this Kits challenge. Our model receives part of body 3D scans as input, and outputs the probability map of the input scans. 2D contexts of intra-slices are extracted by VGG network, and 3D contexts of inter-slices are presented by concatenating the 2D contexts. Then proposals are extracted by region proposal network (RPN), while 3D context are regarded as auxiliary information for region of interest (ROI) regression, classification and mask generation. Our model has shown promising result for this Kits challenge.

Identifiers
doi: 10.24926/548719.094