End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

TL;DR

This paper introduces an end-to-end adversarial shape learning framework for abdominal organ segmentation, improving accuracy by integrating shape representations with deep learning models.

Contribution

It proposes a novel organ point-network with adversarial training to enhance shape awareness in CNN-based segmentation models.

Findings

Improved Dice scores for spleen and pancreas segmentation.

The shape learning component captures fine-grained surface details.

Multi-task training benefits both shape representation and segmentation accuracy.

Abstract

Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.