Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are limited. Due to the high cost of acquiring pixel-level segmentation annotations on the large-scale pretraining dataset, pretraining with unannotated images is highly desirable. In this work, we propose a novel self-supervised learning strategy named Volume Fusion (VF) for pretraining 3D segmentation models. It fuses several random patches from a foreground sub-volume to a background sub-volume based on a predefined set of discrete fusion coefficients, and forces the model to predict the fusion coefficient of each voxel, which is formulated as a self-supervised segmentation task without manual annotations. Additionally, we propose a novel network architecture based on parallel convolution and transformer blocks that is suitable to be transferred to different downstream segmentation tasks with various scales of organs and lesions. The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models. The code and pretrained model are available at https://github.com/openmedlab/MIS-FM.

翻译：在大规模3D体素数据上预训练有助于提升目标医学图像数据集（训练图像和标注有限）的分割性能。由于在大规模预训练数据集中获取像素级分割标注成本高昂，基于无标注图像的预训练具有重要价值。本文提出一种名为Volume Fusion (VF)的新型自监督学习策略，用于预训练3D分割模型。该方法基于预定义的离散融合系数集合，将前景子体积中的若干随机块融合到背景子体积中，迫使模型预测每个体素的融合系数，从而构建无需人工标注的自监督分割任务。此外，我们提出一种基于并行卷积和Transformer模块的新型网络架构，可适配不同器官和病灶尺度的下游分割任务。该模型在11万张无标注3D CT体素上完成预训练，针对头颈部器官、胸腔/腹腔器官等不同下游分割目标的实验表明，我们的预训练模型显著优于从头训练、多种先进自监督训练方法及分割模型。代码与预训练模型已开源：https://github.com/openmedlab/MIS-FM。