Dense Self-Supervised Learning for Medical Image Segmentation

• URL: http://arxiv.org/abs/2407.20395v1
• Date: Mon, 29 Jul 2024 19:42:22 GMT
• Title: Dense Self-Supervised Learning for Medical Image Segmentation
• Authors: Maxime Seince, Loic Le Folgoc, Luiz Augusto Facury de Souza, Elsa Angelini,
• Abstract summary: We propose Pix2Rep, a self-supervised learning (SSL) approach for few-shot segmentation. It reduces the manual annotation burden by learning powerful pixel-level representations directly from unlabeled images. Results show improved performance compared to existing semi- and self-supervised approaches.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has revolutionized medical image segmentation, but it relies heavily on high-quality annotations. The time, cost and expertise required to label images at the pixel-level for each new task has slowed down widespread adoption of the paradigm. We propose Pix2Rep, a self-supervised learning (SSL) approach for few-shot segmentation, that reduces the manual annotation burden by learning powerful pixel-level representations directly from unlabeled images. Pix2Rep is a novel pixel-level loss and pre-training paradigm for contrastive SSL on whole images. It is applied to generic encoder-decoder deep learning backbones (e.g., U-Net). Whereas most SSL methods enforce invariance of the learned image-level representations under intensity and spatial image augmentations, Pix2Rep enforces equivariance of the pixel-level representations. We demonstrate the framework on a task of cardiac MRI segmentation. Results show improved performance compared to existing semi- and self-supervised approaches; and a 5-fold reduction in the annotation burden for equivalent performance versus a fully supervised U-Net baseline. This includes a 30% (resp. 31%) DICE improvement for one-shot segmentation under linear-probing (resp. fine-tuning). Finally, we also integrate the novel Pix2Rep concept with the Barlow Twins non-contrastive SSL, which leads to even better segmentation performance.

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