Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation

by   Fernando Navarro, et al.

Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis. We address this problem from an organ-specific shape-prior learning perspective. We introduce the idea of complementary-task learning to enforce shape-prior leveraging the existing target labels. We propose two complementary-tasks namely i) distance map regression and ii) contour map detection to explicitly encode the geometric properties of each organ. We evaluate the proposed solution on the public VISCERAL dataset containing CT scans of multiple organs. We report a significant improvement of overall dice score from 0.8849 to 0.9018 due to the incorporation of complementary-task learning.


page 3

page 5

page 7


Deep Reinforcement Learning for Organ Localization in CT

Robust localization of organs in computed tomography scans is a constant...

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

In this work, we propose to resolve the issue existing in current deep l...

FourierNet: Shape-Preserving Network for Henle's Fiber Layer Segmentation in Optical Coherence Tomography Images

The Henle's fiber layer (HFL) in the retina carries valuable information...

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

Automatic localization and segmentation of organs-at-risk (OAR) in CT ar...

Robust and fully automated segmentation of mandible from CT scans

Mandible bone segmentation from computed tomography (CT) scans is challe...

Deep Distance Transform for Tubular Structure Segmentation in CT Scans

Tubular structure segmentation in medical images, e.g., segmenting vesse...

Code Repositories


Source code for the paper "Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation" as described in

view repo