Next Article in Journal
A Review on Ergonomics in Agriculture. Part I: Manual Operations
Next Article in Special Issue
Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?
Previous Article in Journal
Compressed-Sensing Magnetic Resonance Image Reconstruction Using an Iterative Convolutional Neural Network Approach
Previous Article in Special Issue
Image Magnification Based on Bicubic Approximation with Edge as Constraint
Open AccessArticle

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

1
Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
2
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
3
Lady Davis Institute for Medical Research, McGill University, Montreal, QC H3S 1Y9, Canada
4
Department of Computer Science, COMSATS University Islamabad, Lahore Campus 54000, Pakistan
5
Department of Psychology, University of Azad Jammu and Kashmir, Muzaffarabad 131000, Pakistan
6
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 1903; https://doi.org/10.3390/app10061903
Received: 7 February 2020 / Revised: 25 February 2020 / Accepted: 29 February 2020 / Published: 11 March 2020
(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)
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. View Full-Text
Keywords: white matter hyperintensities; segmentation; support vector machines; ResNet; classification white matter hyperintensities; segmentation; support vector machines; ResNet; classification
Show Figures

Figure 1

MDPI and ACS Style

Rathore, S.; Niazi, T.; Iftikhar, M.A.; Singh, A.; Rathore, B.; Bilello, M.; Chaddad, A. Multimodal Ensemble-Based Segmentation of White Matter Lesions and Analysis of Their Differential Characteristics across Major Brain Regions. Appl. Sci. 2020, 10, 1903. https://doi.org/10.3390/app10061903

AMA Style

Rathore S, Niazi T, Iftikhar MA, Singh A, Rathore B, Bilello M, Chaddad A. Multimodal Ensemble-Based Segmentation of White Matter Lesions and Analysis of Their Differential Characteristics across Major Brain Regions. Applied Sciences. 2020; 10(6):1903. https://doi.org/10.3390/app10061903

Chicago/Turabian Style

Rathore, Saima; Niazi, Tamim; Iftikhar, Muhammad A.; Singh, Ashish; Rathore, Batool; Bilello, Michel; Chaddad, Ahmad. 2020. "Multimodal Ensemble-Based Segmentation of White Matter Lesions and Analysis of Their Differential Characteristics across Major Brain Regions" Appl. Sci. 10, no. 6: 1903. https://doi.org/10.3390/app10061903

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop