Semi-Supervised Segmentation via Embedding Matching

• URL: http://arxiv.org/abs/2407.04638v1
• Date: Fri, 5 Jul 2024 16:49:21 GMT
• Title: Semi-Supervised Segmentation via Embedding Matching
• Authors: Weiyi Xie, Nathalie Willems, Nikolas Lessmann, Tom Gibbons, Daniele De Massari,
• Abstract summary: Deep convolutional neural networks are widely used in medical image segmentation but require many labeled images for training. We propose a novel semi-supervised segmentation method that leverages mostly unlabeled images and a small set of labeled images in training. The proposed approach yielded a Hausdorff distance with 95th percentile (HD95) of 3.30 and IoU of 0.929, surpassing existing methods achieving HD95 (4.07) and IoU (0.927) at their best.
• Score: 0.8896991256227597
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep convolutional neural networks are widely used in medical image segmentation but require many labeled images for training. Annotating three-dimensional medical images is a time-consuming and costly process. To overcome this limitation, we propose a novel semi-supervised segmentation method that leverages mostly unlabeled images and a small set of labeled images in training. Our approach involves assessing prediction uncertainty to identify reliable predictions on unlabeled voxels from the teacher model. These voxels serve as pseudo-labels for training the student model. In voxels where the teacher model produces unreliable predictions, pseudo-labeling is carried out based on voxel-wise embedding correspondence using reference voxels from labeled images. We applied this method to automate hip bone segmentation in CT images, achieving notable results with just 4 CT scans. The proposed approach yielded a Hausdorff distance with 95th percentile (HD95) of 3.30 and IoU of 0.929, surpassing existing methods achieving HD95 (4.07) and IoU (0.927) at their best.

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