Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble

• URL: http://arxiv.org/abs/2304.07123v1
• Date: Fri, 14 Apr 2023 13:39:39 GMT
• Title: Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
• Authors: Jiahua Dong, Guohua Cheng, Yue Zhang, Chengtao Peng, Yu Song, Ruofeng Tong, Lanfen Lin, Yen-Wei Chen
• Abstract summary: Multi-organ segmentation is a fundamental task in medical image analysis. Due to expensive labor costs and expertise, the availability of multi-organ annotations is usually limited. We propose a novel dual-stage method that consists of a Model Adaptation stage and a Model Ensemble stage.
• Score: 22.82094545786408
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in multi-organ segmentation tasks. However, due to expensive labor costs and expertise, the availability of multi-organ annotations is usually limited and hence poses a challenge in obtaining sufficient training data for deep learning-based methods. In this paper, we aim to address this issue by combining off-the-shelf single-organ segmentation models to develop a multi-organ segmentation model on the target dataset, which helps get rid of the dependence on annotated data for multi-organ segmentation. To this end, we propose a novel dual-stage method that consists of a Model Adaptation stage and a Model Ensemble stage. The first stage enhances the generalization of each off-the-shelf segmentation model on the target domain, while the second stage distills and integrates knowledge from multiple adapted single-organ segmentation models. Extensive experiments on four abdomen datasets demonstrate that our proposed method can effectively leverage off-the-shelf single-organ segmentation models to obtain a tailored model for multi-organ segmentation with high accuracy.

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