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We propose a novel automatic method for accurate segmentation of the prostatein T2-weighted magnetic resonance imaging (MRI). Our method is based onconvolutional neural networks (CNNs). Because of the large variability in theshape, size, and appearance of the prostate and the scarcity of annotatedtraining data, we suggest training two separate CNNs. A global CNN willdetermine a prostate bounding box, which is then resampled and sent to a localCNN for accurate delineation of the prostate boundary. This way, the local CNNcan effectively learn to segment the fine details that distinguish the prostatefrom the surrounding tissue using the small amount of available training data.To fully exploit the training data, we synthesize additional data by deformingthe training images and segmentations using a learned shape model. We apply theproposed method on the PROMISE12 challenge dataset and achieve state of the artresults. Our proposed method generates accurate, smooth, and artifact-freesegmentations. On the test images, we achieve an average Dice score of 90.6with a small standard deviation of 2.2, which is superior to all previousmethods. Our two-step segmentation approach and data augmentation strategy maybe highly effective in segmentation of other organs from small amounts ofannotated medical images.

A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging