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.

翻译：多器官分割是医学图像分析中的基础任务，旨在识别并分离医学图像中的不同器官。近年来，深度学习的巨大成功推动了其在多器官分割任务中的广泛应用。然而，由于高昂的人力成本和专业要求，多器官标注数据的可用性通常有限，这给基于深度学习方法获取充足训练数据带来了挑战。本文通过整合现成的单器官分割模型，在目标数据集上开发多器官分割模型，从而摆脱对多器官分割标注数据的依赖。为此，我们提出了一种新颖的两阶段方法，包括模型自适应阶段和模型集成阶段。第一阶段增强每个现成分割模型在目标域上的泛化能力，第二阶段则蒸馏并整合多个自适应后的单器官分割模型的知识。在四个腹部数据集上的大量实验表明，所提出的方法能够有效利用现成的单器官分割模型，获得高精度的定制化多器官分割模型。