Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network

Delin An, Pengfei Gu, Milan Sonka, Chaoli Wang, Danny Z. Chen; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3624-3634

Deep learning (DL) methods have shown remarkable successes in medical image segmentation often using large amounts of annotated data for model training. However acquiring a large number of diverse labeled 3D medical image datasets is highly difficult and expensive. Recently mask propagation DL methods were developed to reduce the annotation burden on 3D medical images. For example Sli2Vol [59] proposed a self-supervised framework (SSF) to learn correspondences by matching neighboring slices via slice reconstruction in the training stage; the learned correspondences were then used to propagate a labeled slice to other slices in the test stage. But these methods are still prone to error accumulation due to the inter-slice propagation of reconstruction errors. Also they do not handle discontinuities well which can occur between consecutive slices in 3D images as they emphasize exploiting object continuity. To address these challenges in this work we propose a new SSF called Sli2Vol+ for segmenting any anatomical structures in 3D medical images using only a single annotated slice per training and testing volume. Specifically in the training stage we first propagate an annotated 2D slice of a training volume to the other slices generating pseudo-labels (PLs). Then we develop a novel Object Estimation Guided Correspondence Flow Network to learn reliable correspondences between consecutive slices and corresponding PLs in a self-supervised manner. In the test stage such correspondences are utilized to propagate a single annotated slice to the other slices of a test volume. We demonstrate the effectiveness of our method on various medical image segmentation tasks with different datasets showing better generalizability across different organs modalities and modals. Code is available at https://github.com/adlsn/Sli2Volplus