Self-supervision with Superpixels: Training Few-Shot Medical Image Segmentation Without Annotation
Ouyang C, Biffi C, Chen C, Kart T, Qiu H, Rueckert D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12374 LNCS
Pages Range: 762-780
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Glasgow, GBR
ISBN: 9783030585259
DOI: 10.1007/978-3-030-58526-6_45
Abstract
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for training. However, these methods may not be applicable for medical images due to the lack of annotations. To address this problem we make several contributions: (1) A novel self-supervised FSS framework for medical images in order to eliminate the requirement for annotations during training. Additionally, superpixel-based pseudo-labels are generated to provide supervision; (2) An adaptive local prototype pooling module plugged into prototypical networks, to solve the common challenging foreground-background imbalance problem in medical image segmentation; (3) We demonstrate the general applicability of the proposed approach for medical images using three different tasks: abdominal organ segmentation for CT and MRI, as well as cardiac segmentation for MRI. Our results show that, for medical image segmentation, the proposed method outperforms conventional FSS methods which require manual annotations for training.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., & Rueckert, D. (2020). Self-supervision with Superpixels: Training Few-Shot Medical Image Segmentation Without Annotation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 762-780). Glasgow, GBR: Springer Science and Business Media Deutschland GmbH.
MLA:
Ouyang, Cheng, et al. "Self-supervision with Superpixels: Training Few-Shot Medical Image Segmentation Without Annotation." Proceedings of the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, GBR Ed. Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm, Springer Science and Business Media Deutschland GmbH, 2020. 762-780.
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