Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

• URL: http://arxiv.org/abs/2201.06358v1
• Date: Mon, 17 Jan 2022 11:44:10 GMT
• Title: Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
• Authors: Yiwen Li, Yunguan Fu, Qianye Yang, Zhe Min, Wen Yan, Henkjan Huisman, Dean Barratt, Victor Adrian Prisacariu, Yipeng Hu
• Abstract summary: This work presents the first 3D few-shot interclass segmentation network for medical images. It uses a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. A built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects.
• Score: 13.567073992605797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. We propose an image alignment module registering the predicted segmentation of both query and support data, in a standard prototypical learning algorithm, to a reference atlas space. The built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects, regardless whether they are from the same institution or not. Experimental results demonstrated that the proposed registration-assisted prototypical learning significantly improved segmentation accuracy (p-values<0.01) on query data from a holdout institution, with varying availability of support data from multiple institutions. We also report the additional benefits of the proposed 3D networks with 75% fewer parameters and an arguably simpler implementation, compared with existing 2D few-shot approaches that segment 2D slices of volumetric medical images.

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