Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation

• URL: http://arxiv.org/abs/2208.02105v1
• Date: Wed, 3 Aug 2022 14:35:00 GMT
• Title: Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
• Authors: Youssef Dawoud, Katharina Ernst, Gustavo Carneiro, Vasileios Belagiannis
• Abstract summary: We propose the prediction of edge-based maps for self-supervising the training of the unlabelled images. In our experiments, we show that only a small number of annotated images, e.g. 10% of the original training set, is enough for our approach to reach similar performance as with the fully annotated databases on 1- to 10-shots.
• Score: 16.94384366469512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process. In this work, we relax the labelling requirement by combining self-supervised with semi-supervised learning. We propose the prediction of edge-based maps for self-supervising the training of the unlabelled images, which is combined with the supervised training of a small number of labelled images for learning the segmentation task. In our experiments, we evaluate on a few-shot microscopy image cell segmentation benchmark and show that only a small number of annotated images, e.g. 10% of the original training set, is enough for our approach to reach similar performance as with the fully annotated databases on 1- to 10-shots. Our code and trained models is made publicly available

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