OneSeg: Self-learning and One-shot Learning based Single-slice
Annotation for 3D Medical Image Segmentation
- URL: http://arxiv.org/abs/2309.13671v1
- Date: Sun, 24 Sep 2023 15:35:58 GMT
- Title: OneSeg: Self-learning and One-shot Learning based Single-slice
Annotation for 3D Medical Image Segmentation
- Authors: Yixuan Wu, Bo Zheng, Jintai Chen, Danny Z. Chen, Jian Wu
- Abstract summary: We propose a self-learning and one-shot learning based framework for 3D medical image segmentation by annotating only one slice of each 3D image.
Our approach takes two steps: (1) self-learning of a reconstruction network to learn semantic correspondence among 2D slices within 3D images, and (2) representative selection of single slices for one-shot manual annotation.
Our new framework achieves comparable performance with less than 1% annotated data compared with fully supervised methods.
- Score: 36.50258132379276
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As deep learning methods continue to improve medical image segmentation
performance, data annotation is still a big bottleneck due to the
labor-intensive and time-consuming burden on medical experts, especially for 3D
images. To significantly reduce annotation efforts while attaining competitive
segmentation accuracy, we propose a self-learning and one-shot learning based
framework for 3D medical image segmentation by annotating only one slice of
each 3D image. Our approach takes two steps: (1) self-learning of a
reconstruction network to learn semantic correspondence among 2D slices within
3D images, and (2) representative selection of single slices for one-shot
manual annotation and propagating the annotated data with the well-trained
reconstruction network. Extensive experiments verify that our new framework
achieves comparable performance with less than 1% annotated data compared with
fully supervised methods and generalizes well on several out-of-distribution
testing sets.
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