Details

Autor(en) / Beteiligte

• Wu, Yixuan
• Zheng, Bo
• Chen, Jintai
• Chen, Danny Z.
• Wu, Jian
• Li, Shuo
• Dou, Qi
• Speidel, Stefanie
• Fletcher, P. Thomas
• Wang, Linwei

Titel

Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D Medical Image Segmentation

Ist Teil von

• Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, p.244-254

Ort / Verlag

Cham: Springer Nature Switzerland

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\%$$\end{document} annotated data compared with fully supervised methods and generalizes well on several out-of-distribution testing sets.