Separated collaborative learning for semi-supervised prostate segmentation with multi-site heterogeneous unlabeled MRI data.

Segmenting prostate from magnetic resonance imaging (MRI) is a critical procedure in prostate cancer staging and treatment planning. Considering the nature of labeled data scarcity for medical images, semi-supervised learning (SSL) becomes an appealing solution since it can simultaneously exploit limited labeled data and a large amount of unlabeled data. However, SSL relies on the assumption that the unlabeled images are abundant, which may not be satisfied when the local institute has limited image collection capabilities. An intuitive solution is to seek support from other centers to enrich the unlabeled image pool. However, this further introduces data heterogeneity, which can impede SSL that works under identical data distribution with certain model assumptions. Aiming at this under-explored yet valuable scenario, in this work, we propose a separated collaborative learning (SCL) framework for semi-supervised prostate segmentation with multi-site unlabeled MRI data. Specifically, on top of the teacher-student framework, SCL exploits multi-site unlabeled data by: (i) Local learning, which advocates local distribution fitting, including the pseudo label learning that reinforces confirmation of low-entropy easy regions and the cyclic propagated real label learning that leverages class prototypes to regularize the distribution of intra-class features; (ii) External multi-site learning, which aims to robustly mine informative clues from external data, mainly including the local-support category mutual dependence learning, which takes the spirit that mutual information can effectively measure the amount of information shared by two variables even from different domains, and the stability learning under strong adversarial perturbations to enhance robustness to heterogeneity. Extensive experiments on prostate MRI data from six different clinical centers show that our method can effectively generalize SSL on multi-site unlabeled data and significantly outperform other semi-supervised segmentation methods. Besides, we validate the extensibility of our method on the multi-class cardiac MRI segmentation task with data from four different clinical centers. • We present a new and practical scenario of multi-site semi-supervised learning (MS-SSL), which allows the enrichment of the unlabeled pool with heterogeneous unlabeled data from multiple arbitrary sites and support the semi-supervised learning in local centers. • We propose a new separated collaborative learning (SCL) framework, including local learning and external multi-site learning, for this under-explored scenario. • We propose a novel local-support category mutual dependence learning scheme, which advocates mutual information-based distribution-insensitive relationship modeling on region-of-interests, for effective collaboration between local labeled data and heterogeneous external unlabeled data to support local learning. • Our method is extensively evaluated on public prostate MRI datasets from six different institutes with varying scanning protocols and patient demographics. The experimental results demonstrate the superiority of our approach. • We also validated the extensibility of our method on the multi-class cardiac MRI segmentation task with data from four different clinical centers. [ABSTRACT FROM AUTHOR]