Comparison of Multi-atlas Segmentation and U-Net Approaches for Automated 3D Liver Delineation in MRI.

Segmentation of medical images is typically one of the first and most critical steps in medical image analysis. Manual segmentation of volumetric images is labour-intensive and prone to error. Automated segmentation of images mitigates such issues. Here, we compare the more conventional registration-based multi-atlas segmentation technique with recent deep-learning approaches. Previously, 2D U-Nets have commonly been thought of as more appealing than their 3D versions; however, recent advances in GPU processing power, memory, and availability have enabled deeper 3D networks with larger input sizes. We evaluate methods by comparing automated liver segmentations with gold standard manual annotations, in volumetric MRI images. Specifically, 20 expert-labelled ground truth liver labels were compared with their automated counterparts. The data used is from a liver cancer study, HepaT1ca, and as such, presents an opportunity to work with a varied and challenging dataset, consisting of subjects with large anatomical variations responding from different tumours and resections. Deep-learning methods (3D and 2D U-Nets) proved to be significantly more effective at obtaining an accurate delineation of the liver than the multi-atlas implementation. 3D U-Net was the most successful of the methods, achieving a median Dice score of 0.970. 2D U-Net and multi-atlas based segmentation achieved median Dice scores of 0.957 and 0.931, respectively. Multi-atlas segmentation tended to overestimate total liver volume when compared with the ground truth, while UNet approaches tended to slightly underestimate the liver volume. Both UNet approaches were also much quicker, taking around one minute, compared with close to one hour for the multi-atlas approach.