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

## TL;DR

This paper introduces a shape-aware complementary-task learning approach for multi-organ segmentation in CT scans, explicitly encoding organ geometry to improve segmentation accuracy.

## Contribution

It proposes a novel complementary-task learning framework with distance map regression and contour detection to incorporate shape priors into multi-organ segmentation.

## Key findings

- Achieved a significant increase in dice score from 0.8849 to 0.9018.
- Demonstrated improved segmentation performance on the VISCERAL dataset.
- Validated the effectiveness of shape-aware complementary tasks in medical image segmentation.

## Abstract

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.