Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

TL;DR

This paper introduces OrganNet, a novel one-shot organ segmentation method that imitates radiologists' reasoning by modeling anatomical correlations, achieving state-of-the-art results even with limited annotated data.

Contribution

OrganNet is the first model to integrate anatomical correlation modeling into one-shot segmentation, enabling effective generalization to unseen organs.

Findings

OrganNet outperforms existing methods in one-shot segmentation accuracy.

It maintains high performance despite significant organ morphology variations.

OrganNet achieves competitive results compared to fully-supervised models.

Abstract

Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic. We propose pyramid reasoning modules (PRMs) to model the anatomical correlation between anchor and target volumes. In practice, the proposed module first computes a correlation matrix between target and anchor computerized tomography (CT) volumes. Then, this matrix is used to transform the feature representations of both anchor volume and its segmentation mask. Finally, OrganNet learns to fuse the representations from various inputs and predicts segmentation results for target volume. Extensive experiments show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task. Moreover, even when compared with fully-supervised segmentation models, OrganNet is still able to produce satisfying segmentation results.