Semi-Supervised Segmentation via Embedding Matching
<|reference_start|>Semi-Supervised Segmentation via Embedding Matching: 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.<|reference_end|>
@article{xie2024semi-supervised,
    title={Semi-Supervised Segmentation via Embedding Matching},
    author={Weiyi Xie, Nathalie Willems, Nikolas Lessmann, Tom Gibbons and Daniele
  De Massari},
    journal={arXiv preprint arXiv:2407.04638},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04638},
    primaryClass={cs.CV}
}