Fully automated analysis using BRAINS: AutoWorkup

Abstract

The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over 20 years. The original neuroimage analysis pipeline using BRAINS was designed as a semiautomated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling, and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80, and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans, the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multivendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.

Fig. 1

The template created from 108 T1 scans. The axial view shows in yellow the outline of the brain mask created by thresholding the brain probability at 0.5.

Fig. 2

Final Review of AutoWorkup results and masks created by use of artificial neural networks. The upper right view shows the discrete-classified image; the other views are displaying the continuous-classified image. The scan was acquired on a 3T Siemens Trio scanner.