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In the paper, we present an approach for learning a single model thatuniversally segments 33 anatomical structures, including vertebrae, pelvicbones, and abdominal organs. Our model building has to address the followingchallenges. Firstly, while it is ideal to learn such a model from alarge-scale, fully-annotated dataset, it is practically hard to curate such adataset. Thus, we resort to learn from a union of multiple datasets, with eachdataset containing the images that are partially labeled. Secondly, along theline of partial labelling, we contribute an open-source, large-scale vertebrasegmentation dataset for the benefit of spine analysis community, CTSpine1K,boasting over 1,000 3D volumes and over 11K annotated vertebrae. Thirdly, in a3D medical image segmentation task, due to the limitation of GPU memory, wealways train a model using cropped patches as inputs instead a whole 3D volume,which limits the amount of contextual information to be learned. To this, wepropose a cross-patch transformer module to fuse more information in adjacentpatches, which enlarges the aggregated receptive field for improvedsegmentation performance. This is especially important for segmenting, say, theelongated spine. Based on 7 partially labeled datasets that collectivelycontain about 2,800 3D volumes, we successfully learn such a universal model.Finally, we evaluate the universal model on multiple open-source datasets,proving that our model has a good generalization performance and canpotentially serve as a solid foundation for downstream tasks.

Universal Segmentation of 33 Anatomies