Kidney and Tumor Segmentation Using Modified 3D Mask RCNN

Authors
Chen, Cong
Ma, Longfei
Jia, Yan
Zuo, Panli

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Detection of kidney tumors and accurate evaluation of their size are crucial for tracking cancer progression. Automating 3D volume detection and segmentation can improve workflow as well as patient care. We adapt the state of the art architecture for 2D object detection and segmentation, Mask RCNN, to handle 3D images and employ it along with U-net to detect and segment kidney and kidney tumor from CT scans. We report on competitive results for the kidney segmentation and kidney tumor segmentation on the 2019 Kidney Tumor Segmentation Challenge data set.

Identifiers
doi: 10.24926/548719.061