bibtext: @InProceedings{ Zhe_FewShot_MICCAI2024,
                   author = { Zheng, Meng and Planche, Benjamin and Gao, Zhongpai and Chen, Terrence and Radke, Richard J. and Wu, Ziyan },
                   title = { { Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Conventional 3D medical image segmentation methods typically require learning heavy 3D networks (e.g., 3D-UNet), as well as large amounts of in-domain data with accurate pixel/voxel-level labels to avoid overfitting. These solutions are thus extremely time- and labor-expensive, but also may easily fail to generalize to unseen objects during training. To alleviate this issue, we present MSFSeg, a novel few-shot 3D segmentation framework with a lightweight multi-surrogate fusion (MSF). MSFSeg is able to automatically segment unseen 3D objects/organs (during training) provided with one or a few annotated 2D slices or 3D sequence segments, via learning dense query-support organ/lesion anatomy correlations across patient populations.
  Our proposed MSF module mines comprehensive and diversified morphology correlations between unlabeled and the few labeled slices/sequences through multiple designated surrogates, making it able to generate accurate cross-domain 3D segmentation masks given annotated slices or sequences. We demonstrate the effectiveness of our proposed framework by showing superior performance on conventional few-shot segmentation benchmarks compared to prior art, and remarkable cross-domain cross-volume segmentation performance on proprietary 3D segmentation datasets for challenging entities, i.e. tubular structures, with only limited 2D or 3D labels.
title: Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion
authors: Zheng, Meng Planche, Benjamin Gao, Zhongpai Chen, Terrence Radke, Richard J. Wu, Ziyan