Self-Supervised Learning for Few-Shot Medical Image Segmentation

Ouyang C, Biffi C, Chen C, Kart T, Qiu H, Rueckert D (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 41

Pages Range: 1837-1848

Journal Issue: 7

DOI: 10.1109/TMI.2022.3150682

Abstract

Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflexibility by learning to segment an arbitrary unseen semantically meaningful class by referring to only a few labeled examples, without involving fine-tuning. State-of-the-art FSS methods are typically designed for segmenting natural images and rely on abundant annotated data of training classes to learn image representations that generalize well to unseen testing classes. However, such a training mechanism is impractical in annotation-scarce medical imaging scenarios. To address this challenge, in this work, we propose a novel self-supervised FSS framework for medical images, named SSL-ALPNet, in order to bypass the requirement for annotations during training. The proposed method exploits superpixel-based pseudo-labels to provide supervision signals. In addition, we propose a simple yet effective adaptive local prototype pooling module which is plugged into the prototype networks to further boost segmentation accuracy. We demonstrate the general applicability of the proposed approach using three different tasks: organ segmentation of abdominal CT and MRI images respectively, and cardiac segmentation of MRI images. The proposed method yields higher Dice scores than conventional FSS methods which require manual annotations for training in our experiments.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., & Rueckert, D. (2022). Self-Supervised Learning for Few-Shot Medical Image Segmentation. IEEE Transactions on Medical Imaging, 41(7), 1837-1848. https://doi.org/10.1109/TMI.2022.3150682

MLA:

Ouyang, Cheng, et al. "Self-Supervised Learning for Few-Shot Medical Image Segmentation." IEEE Transactions on Medical Imaging 41.7 (2022): 1837-1848.

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