Semi-Supervised Segmentation via Embedding Matching
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916313606127616 |
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| author | Xie, Weiyi Willems, Nathalie Lessmann, Nikolas Gibbons, Tom De Massari, Daniele |
| author_facet | Xie, Weiyi Willems, Nathalie Lessmann, Nikolas Gibbons, Tom De Massari, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semi-Supervised Segmentation via Embedding Matching Xie, Weiyi Willems, Nathalie Lessmann, Nikolas Gibbons, Tom De Massari, Daniele Computer Vision and Pattern Recognition I.5.4; I.4.6; I.2.10 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. |
| title | Semi-Supervised Segmentation via Embedding Matching |
| topic | Computer Vision and Pattern Recognition I.5.4; I.4.6; I.2.10 |
| url | https://arxiv.org/abs/2407.04638 |