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Precise delineation of multiple organs or abnormal regions in the human bodyfrom medical images plays an essential role in computer-aided diagnosis,surgical simulation, image-guided interventions, and especially in radiotherapytreatment planning. Thus, it is of great significance to explore automaticsegmentation approaches, among which deep learning-based approaches haveevolved rapidly and witnessed remarkable progress in multi-organ segmentation.However, obtaining an appropriately sized and fine-grained annotated dataset ofmultiple organs is extremely hard and expensive. Such scarce annotation limitsthe development of high-performance multi-organ segmentation models butpromotes many annotation-efficient learning paradigms. Among these, studies ontransfer learning leveraging external datasets, semi-supervised learning usingunannotated datasets and partially-supervised learning integratingpartially-labeled datasets have led the dominant way to break such dilemma inmulti-organ segmentation. We first review the traditional fully supervisedmethod, then present a comprehensive and systematic elaboration of the 3abovementioned learning paradigms in the context of multi-organ segmentationfrom both technical and methodological perspectives, and finally summarizetheir challenges and future trends.

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation