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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Acknowledgments
This research was partially supported by National Key R\( { \& }\)D Program of China under grant No. 2019YFC0118802, National Natural Science Foundation of China under grants No. 62176231, Zhejiang public welfare technology research project under grant No. LGF20F020013, D. Z. Chen’s research was supported in part by NSF Grant CCF-1617735.
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Wu, Y., Zheng, B., Chen, J., Chen, D.Z., Wu, J. (2022). Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D Medical Image Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham. https://doi.org/10.1007/978-3-031-16452-1_24
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