A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis

Mostafa Salem*, Mariano Cabezas, Sergi Valverde, Deborah Pareto, Arnau Oliver, Joaquim Salvi, Àlex Rovira, Xavier Lladó

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)
2 Downloads (Pure)

Abstract

Introduction Longitudinal magnetic resonance imaging (MRI) analysis has an important role in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain MRI scans is considered a prognostic and predictive biomarker for the disease. In this study, we propose a supervised approach for detecting new T2-w lesions using features from image intensities, subtraction values, and deformation fields (DF). Methods One year apart multi-channel brain MRI scans were obtained for 60 patients, 36 of them with new T2-w lesions. Images from both temporal points were preprocessed and co-registered. Afterwards, they were registered using multi-resolution affine registration, allowing their subtraction. In particular, the DFs between both images were computed with the Demons non-rigid registration algorithm. Afterwards, a logistic regression model was trained with features from image intensities, subtraction values, and DF operators. We evaluated the performance of the model following a leave-one-out cross-validation scheme. Results In terms of detection, we obtained a mean Dice similarity coefficient of 0.77 with a true-positive rate of 74.30% and a false-positive detection rate of 11.86%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.56. The performance of our model was significantly higher than state-of-the-art methods. Conclusions The performance of the proposed method shows the benefits of using DF operators as features to train a supervised learning model. Compared to other methods, the proposed model decreases the number of false-positives while increasing the number of true-positives, which is relevant for clinical settings.

Original languageEnglish
Pages (from-to)607-615
Number of pages9
JournalNeuroImage: Clinical
Volume17
DOIs
Publication statusPublished - 2018
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • automatic new lesion detection
  • brain
  • machine learning
  • MRI
  • multiple sclerosis

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