A spatial guided self-supervised clustering network for medical image segmentation

Ahn, Euijoon, Feng, Dagan, and Kim, Jinman (2021) A spatial guided self-supervised clustering network for medical image segmentation. In: Lecture Notes in Computer Science (12901) pp. 379-388. From: MICCAI 2021: International Conference on Medical Image Computing and Computer-Assisted Intervention, 27 September - 1 October 2021, Strasbourg, France.

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The segmentation of medical images is a fundamental step in automated clinical decision support systems. Existing medical image segmentation methods based on supervised deep learning, however, remain problematic because of their reliance on large amounts of labelled training data. Although medical imaging data repositories continue to expand, there has not been a commensurate increase in the amount of annotated data. Hence, we propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation, where we introduce multiple loss functions designed to aid in grouping image pixels that are spatially connected and have similar feature representations. It iteratively learns feature representations and clustering assignment of each pixel in an end-to-end fashion from a single image. We also propose a context-based consistency loss that better delineates the shape and boundaries of image regions. It enforces all the pixels belonging to a cluster to be spatially close to the cluster centre.We evaluated our method on 2 public medical image datasets and compared it to existing conventional and self-supervised clustering methods. Experimental results show that our method was most accurate for medical image segmentation.

Item ID: 72027
Item Type: Conference Item (Research - E1)
ISBN: 978-3-030-87192-5
ISSN: 0302-9743
Copyright Information: © Springer Nature Switzerland AG 2021
Date Deposited: 22 Feb 2022 23:06
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 40%
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 60%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220408 Information systems @ 100%
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