A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis
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 ap...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018-01-01
|
| Series: | NeuroImage: Clinical |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158217302954 |
| _version_ | 1828770702097907712 |
|---|---|
| author | Mostafa Salem Mariano Cabezas Sergi Valverde Deborah Pareto Arnau Oliver Joaquim Salvi Àlex Rovira Xavier Lladó |
| author_facet | Mostafa Salem Mariano Cabezas Sergi Valverde Deborah Pareto Arnau Oliver Joaquim Salvi Àlex Rovira Xavier Lladó |
| author_sort | Mostafa Salem |
| collection | DOAJ |
| description | 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. Keywords: Brain, MRI, Multiple sclerosis, Automatic new lesion detection, Machine learning |
| first_indexed | 2024-12-11T14:11:53Z |
| format | Article |
| id | doaj.art-3e04124daa58414a8f7540c407039df9 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-11T14:11:53Z |
| publishDate | 2018-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-3e04124daa58414a8f7540c407039df92022-12-22T01:03:23ZengElsevierNeuroImage: Clinical2213-15822018-01-0117607615A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosisMostafa Salem0Mariano Cabezas1Sergi Valverde2Deborah Pareto3Arnau Oliver4Joaquim Salvi5Àlex Rovira6Xavier Lladó7Research Institute of Computer Vision and Robotics, University of Girona, Spain; Computer Science Department, Faculty of Computers and Information, Assiut University, Egypt; Corresponding author at: Ed. P-IV, Campus Montilivi, University of Girona, 17003 Girona, Spain.Research Institute of Computer Vision and Robotics, University of Girona, SpainResearch Institute of Computer Vision and Robotics, University of Girona, SpainMagnetic Resonance Unit, Dept of Radiology, Vall d’Hebron University Hospital, SpainResearch Institute of Computer Vision and Robotics, University of Girona, SpainResearch Institute of Computer Vision and Robotics, University of Girona, SpainMagnetic Resonance Unit, Dept of Radiology, Vall d’Hebron University Hospital, SpainResearch Institute of Computer Vision and Robotics, University of Girona, SpainIntroduction: 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. Keywords: Brain, MRI, Multiple sclerosis, Automatic new lesion detection, Machine learninghttp://www.sciencedirect.com/science/article/pii/S2213158217302954 |
| spellingShingle | Mostafa Salem Mariano Cabezas Sergi Valverde Deborah Pareto Arnau Oliver Joaquim Salvi Àlex Rovira Xavier Lladó A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis NeuroImage: Clinical |
| title | A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis |
| title_full | A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis |
| title_fullStr | A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis |
| title_full_unstemmed | A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis |
| title_short | A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis |
| title_sort | supervised framework with intensity subtraction and deformation field features for the detection of new t2 w lesions in multiple sclerosis |
| url | http://www.sciencedirect.com/science/article/pii/S2213158217302954 |
| work_keys_str_mv | AT mostafasalem asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT marianocabezas asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT sergivalverde asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT deborahpareto asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT arnauoliver asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT joaquimsalvi asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT alexrovira asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT xavierllado asupervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT mostafasalem supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT marianocabezas supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT sergivalverde supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT deborahpareto supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT arnauoliver supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT joaquimsalvi supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT alexrovira supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis AT xavierllado supervisedframeworkwithintensitysubtractionanddeformationfieldfeaturesforthedetectionofnewt2wlesionsinmultiplesclerosis |