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In this paper, we propose a novel technique for sampling sequential imagesusing a cylindrical transform in a cylindrical coordinate system for kidneysemantic segmentation in abdominal computed tomography (CT). The imagesgenerated from a cylindrical transform augment a limited annotated set ofimages in three dimensions. This approach enables us to train contemporaryclassification deep convolutional neural networks (DCNNs) instead of fullyconvolutional networks (FCNs) for semantic segmentation. Typical semanticsegmentation models segment a sequential set of images (e.g. CT or video) bysegmenting each image independently. However, the proposed method not onlyconsiders the spatial dependency in the x-y plane, but also the spatialsequential dependency along the z-axis. The results show that classificationDCNNs, trained on cylindrical transformed images, can achieve a highersegmentation performance value than FCNs using a limited number of annotatedimages.

Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images