Multimodal Ensemble-Based Segmentation of White Matter Lesions and Analysis of Their Differential Characteristics across Major Brain Regions

• Published in: Applied sciences Vol. 10; no. 6; p. 1903
• Main Authors: Rathore, Saima, Niazi, Tamim, Iftikhar, Muhammad Aksam, Singh, Ashish, Rathore, Batool, Bilello, Michel, Chaddad, Ahmad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Automation; Blood vessels; Brain; Classification; Datasets; Deep learning; Heterogeneity; Image processing; In vivo methods and tests; Ischemia; Lesions; Machine learning; Magnetic resonance imaging; Methods; Neural networks; Patients; Post-production processing; Precision medicine; Radiomics; resnet; Scanners; segmentation; Signal strength; Spatial distribution; Substantia alba; Support vector machines; Temporal lobe; Transfer learning; white matter hyperintensities
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10061903

White matter lesions (WML) are common in a variety of brain pathologies, including ischemia affecting blood vessels deeper inside the brain’s white matter, and show an abnormal signal in T1-weighted and FLAIR images. The emergence of personalized medicine requires quantification and analysis of differential characteristics of WML across different brain regions. Manual segmentation and analysis of WMLs is laborious and time-consuming; therefore, automated methods providing robust, reproducible, and fast WML segmentation and analysis are highly desirable. In this study, we tackled the segmentation problem as a voxel-based classification problem. We developed an ensemble of different classification models, including six models of support vector machine, trained on handcrafted and transfer learning features, and five models of Residual neural network, trained on varying window sizes. The output of these models was combined through majority-voting. A series of image processing operations was applied to remove false positives in a post-processing step. Moreover, images were mapped to a standard atlas template to quantify the spatial distribution of WMLs, and a radiomic analysis of all the lesions across different brain regions was carried out. The performance of the method on multi-institutional WML Segmentation Challenge dataset (n = 150) comprising T1-weighted and FLAIR images was >90% within data of each institution, multi-institutional data pooled together, and across-institution training–testing. Forty-five percent of lesions were found in the temporal lobe of the brain, and these lesions were easier to segment (95.67%) compared to lesions in other brain regions. Lesions in different brain regions were characterized by their differential characteristics of signal strength, size/shape, heterogeneity, and texture (p < 0.001). The proposed multimodal ensemble-based segmentation of WML showed effective performance across all scanners. Further, the radiomic characteristics of WMLs of different brain regions provide an in vivo portrait of phenotypic heterogeneity in WMLs, which points to the need for precision diagnostics and personalized treatment.