Automated tissue segmentation of MR brain images in the presence of white matter lesions

• Published in: Medical image analysis 2017-01, Vol.35, p.446-457
• Main Authors: Valverde, Sergi, Oliver, Arnau, Roura, Eloy, González-Villà, Sandra, Pareto, Deborah, Vilanova, Joan C., Ramió-Torrentà, Lluís, Rovira, Àlex, Lladó, Xavier
• Format: Article
• Language: English
• Subjects: Algorithms; Annotations; Automatic tissue segmentation; Automation - methods; Brain; Brain - pathology; Downloading; Humans; Image processing; Image segmentation; Lesions; Magnetic Resonance Imaging - methods; MRI; Multiple sclerosis; Multiple Sclerosis - pathology; Neuroimaging; NMR; Nuclear magnetic resonance; Pipelines; Probability; Refilling; State of the art; Substantia alba; White Matter - pathology
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2016.08.014

•We propose an automated brain tissue segmentation method for MS images with lesions.•The approach relies only on T1-w to segment brain tissue but FLAIR is recommended.•We evaluate its accuracy with both the MRBrainS13 challenge and MS data.•Our approach was the best unsupervised ranked method of the challenge (7th position / 31).•With MS data, the performance was similar to or better than the state-of-the-art. [Display omitted] Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community.