A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images
This work provides an automated segmentation tool for radiologists, improving efficiency in prostate cancer diagnosis by reducing the need for time-consuming manual annotation.
This paper proposes a fully automated cascaded deep learning architecture, Cascaded MRes-UNET, for segmenting the prostate gland and peripheral zone in T2-weighted 3D Fast Spin Echo Images. The method achieved high Dice scores (0.91±.02), precision (0.91±.04), and recall (0.92±.03) for prostate segmentation, with less than 5% average difference in total prostate volume estimation compared to manual annotations.
Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer. Automated segmentation of the prostate eliminates the need for manual annotation by a radiologist which is time consuming. This improves efficiency in the extraction of imaging features for the characterization of prostate tissues. In this work, we propose a fully automated cascaded deep learning architecture with residual blocks, Cascaded MRes-UNET, for segmentation of the prostate gland and the peripheral zone in one pass through the network. The network yields high Dice scores ($0.91\pm.02$), precision ($0.91\pm.04$), and recall scores ($0.92\pm.03$) in prostate segmentation compared to manual annotations by an experienced radiologist. The average difference in total prostate volume estimation is less than 5%.