OneSeg：基于自学习与单样本学习的单层切片标注用于3D医学图像分割
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
AI总结：
提出OneSeg框架，通过自学习重建网络建立切片语义对应并传播单切片标注，以少于1%标注实现媲美全监督的3D医学图像分割。
摘要：
随着深度学习方法不断提升医学图像分割性能，数据标注仍是一大瓶颈，因为这会给医学专家带来劳动密集且耗时的负担，对于3D图像尤其如此。为了在显著减少标注工作量的同时获得具有竞争力的分割精度，我们提出了一种基于自学习和单样本学习的3D医学图像分割框架，只需为每个3D图像标注一个切片。我们的方法分为两步：（1）自学习一个重建网络，以学习3D图像内各2D切片之间的语义对应关系；（2）对单个切片进行代表性选择，用于单样本人工标注，并利用训练良好的重建网络传播已标注数据。大量实验验证，我们的新框架在使用少于1%标注数据的情况下取得了与全监督方法相当的性能，并且在多个分布外测试集上具有良好的泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
