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

TL;DR

This paper introduces a CNN-based two-step method with data augmentation for accurate prostate segmentation in T2-weighted MRI, achieving state-of-the-art results on a public dataset.

Contribution

It presents a novel two-stage CNN approach combined with data synthesis to improve prostate segmentation accuracy with limited training data.

Findings

Achieved a Dice score of 90.6 on the PROMISE12 dataset.

Outperformed all previous methods in prostate segmentation accuracy.

Generated smooth, artifact-free segmentation results.

Abstract

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