  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.
