Automatic prostate segmentation of magnetic resonance imaging using Res-Net

Segmenting the prostate from magnetic resonance images plays an important role in prostate cancer diagnosis and in evaluating the treatment response. However, the lack of a clear prostate boundary, heterogeneity of prostate tissue, large variety of prostate shape and scarcity of annotated training data makes automatic segmentation a very challenging task. In this work, we proposed a novel two stage segmentation method to automatically segment prostate to support accurate and reproducible results with multisite and multivendor dataset. In the proposed method, we use the combination U-Net with residual blocks.

The proposed method comprises two stage neural network, first is 2D U-Net, used find the approximate location of prostate, the second is the combination of U-Net and Res-Net used for accurate segmentation of prostate. The network was trained on 116 patient datasets from three publicly available data sources. 80% of data is used for training, 10% for validation, and 10% for testing. The commonly used segmentation evaluation metrics Dice similarity coefficient (DSC), Sensitivity, and Specificity are used for quantitative evaluation of the network.

With the proposed method average DSC value of 93.8%, Sensitivity value of 94.6% and Specificity of 99.3% was achieved on test datasets.

Our experimental results show that the segmentation accuracy can be improved significantly using two stage neural networks.