A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images

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%.

A Comparison of Deep Learning Convolution Neural Networks for Liver Segmentation in Radial Turbo Spin Echo Images

Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. In this work, we use a deep-learning convolutional neural network (CNN) for the segmentation of liver in abdominal RADTSE images. A modified UNET architecture with generalized dice loss objective function was implemented. Three 2D CNNs were trained, one for each image type obtained from the RADTSE sequence. On evaluating the performance of the CNNs on the validation set, we found that CNNs trained on TE images or the T2 maps had higher average dice scores than the composite images. This, in turn, implies that the information regarding T2 variation in tissues aids in improving the segmentation performance.