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A 2.5D Cancer Segmentation for MRI Images Based on U-Net

Existing image segmentation methods are mostly confined to the 2D plane, which only considers image information in one direction. As a classic image segmentation framework with 2D CNN framework, U-Net needs more improvement in segmentation accuracy. Moreover, 3D CNNs require heavy computational cost. In order to balance between the segmentation accuracy and the computational cost, this paper mainly proposes a 2.5D image segmentation method based on the U-Net for the prediction of cancer's area of MRI in nasopharyngeal carcinoma. The paper has sampled 2D patches from 3D MRI images with three orthogonal directions, and then fed into three U-Nets separately. Finally, the probability maps obtained from the trained U-Nets have been integrated to generate the final segmentation results. The experiments have demonstrated that the segmentation performance of our 2.5D method has been significantly improved compared to 2D segmentation and it is superior to 3D U-net on the computation efficiency.