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The segmentation of medical images is a fundamental step in automatedclinical decision support systems. Existing medical image segmentation methodsbased on supervised deep learning, however, remain problematic because of theirreliance on large amounts of labelled training data. Although medical imagingdata repositories continue to expand, there has not been a commensurateincrease in the amount of annotated data. Hence, we propose a new spatialguided self-supervised clustering network (SGSCN) for medical imagesegmentation, where we introduce multiple loss functions designed to aid ingrouping image pixels that are spatially connected and have similar featurerepresentations. It iteratively learns feature representations and clusteringassignment of each pixel in an end-to-end fashion from a single image. We alsopropose a context-based consistency loss that better delineates the shape andboundaries of image regions. It enforces all the pixels belonging to a clusterto be spatially close to the cluster centre. We evaluated our method on 2public medical image datasets and compared it to existing conventional andself-supervised clustering methods. Experimental results show that our methodwas most accurate for medical image segmentation.

A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation