Abstract：Abdominal multi-organ segmentation of medical images is essential for clinical applications such as surgical treatment planning and assisted diagnosis. Most published medical image datasets label partial organs only, which is difficult for accurate multiple organs segmentation of medical images, thus the segmentation model developed by this approach is not generally applicable. In this paper, we proposed a joint-dataset-based multi-organ segmentation abdominal network: C2F-MSNet, which contained coarse segment and fine segment. During coarse segmentation, the explicit conditional control module was employed for the training of the network on multiple partially labeled datasets, while the self-attention module and the deep supervision strategy were implemented. During the fine segmentation, the fine segmentation area was indexed by rough segmentation results, and the fine segmentation was guided and the multi-scale fine segmentation network was constructed. Experiments were performed on 663 CT data obtained from KiTS, Decathlon-liver, Decathlon-spleen and Decathlon-pancreas datasets, evaluated by dice similarity coefficient (DSC) and Hausdorff distance (HD). The results of DSC reached 0.967, 0.964, 0.956, and 0.838 for kidney, liver, spleen, and pancreas, respectively, and that of HD reached 12.51, 25.02, 6.68, and 12.58, respectively. Experiment results showed that the C2F-MSNet effectively solved the training problem of multiple partially labeled datasets and achieved accurate multi-organ segmentation in the joint datasets.
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