Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort

Martina Greselin; Po-Jui Lu; Lester Melie-Garcia; Mario Ocampo-Pineda; Riccardo Galbusera; Alessandro Cagol; Matthias Weigel; Nina de Oliveira Siebenborn; Esther Ruberte; Pascal Benkert; Stefanie Müller; Sebastian Finkener; Jochen Vehoff; Giulio Disanto; Oliver Findling; Andrew Chan; Anke Salmen; Caroline Pot; Claire Bridel; Chiara Zecca; Tobias Derfuss; Johanna M Lieb; Michael Diepers; Franca Wagner; Maria I Vargas; Renaud Du Pasquier; Patrice H Lalive; Emanuele Pravatà; Johannes Weber; Claudio Gobbi; David Leppert; Olaf Chan-Hi Kim; Philippe C Cattin; Robert Hoepner; Patrick Roth; Ludwig Kappos; Jens Kuhle; Cristina Granziera

doi:10.3390/bioengineering11080858

Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort

Bioengineering (Basel). 2024 Aug 22;11(8):858. doi: 10.3390/bioengineering11080858.

Authors

Martina Greselin^{1

2

3}, Po-Jui Lu^{1

2

3}, Lester Melie-Garcia^{1

2

3}, Mario Ocampo-Pineda^{1

2

3}, Riccardo Galbusera^{1

2

3}, Alessandro Cagol^{1

2

3

4}, Matthias Weigel^{1

2

3

5}, Nina de Oliveira Siebenborn^{1

2

3

6}, Esther Ruberte^{1

2

3

6}, Pascal Benkert⁷, Stefanie Müller⁸, Sebastian Finkener⁹, Jochen Vehoff⁸, Giulio Disanto¹⁰, Oliver Findling⁹, Andrew Chan¹¹, Anke Salmen^{11

12}, Caroline Pot¹³, Claire Bridel¹⁴, Chiara Zecca^{10

15}, Tobias Derfuss³, Johanna M Lieb¹⁶, Michael Diepers¹⁷, Franca Wagner¹⁸, Maria I Vargas¹⁹, Renaud Du Pasquier¹³, Patrice H Lalive¹⁴, Emanuele Pravatà^{15

20}, Johannes Weber²¹, Claudio Gobbi^{10

15}, David Leppert³, Olaf Chan-Hi Kim²¹, Philippe C Cattin²², Robert Hoepner¹¹, Patrick Roth²³, Ludwig Kappos^{1

2

3}, Jens Kuhle^{2

3}, Cristina Granziera^{1

2

3}

Affiliations

¹ Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, 4123 Basel, Switzerland.
² Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland.
³ Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland.
⁴ Department of Health Sciences, University of Genova, 16132 Genova, Italy.
⁵ Division of Radiological Physics, Department of Radiology, University Hospital Basel, 4031 Basel, Switzerland.
⁶ Medical Image Analysis Center (MIAC), 4051 Basel, Switzerland.
⁷ Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, 4031 Basel, Switzerland.
⁸ Department of Neurology, Cantonal Hospital St. Gallen, 9000 St. Gallen, Switzerland.
⁹ Department of Neurology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland.
¹⁰ Neurology Department, Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland.
¹¹ Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
¹² Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, 44791 Bochum, Germany.
¹³ Service of Neurology, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, 1005 Lausanne, Switzerland.
¹⁴ Division of Neurology, Department of Clinical Neurosciences, Faculty of Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland.
¹⁵ Faculty of biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland.
¹⁶ Division of Diagnostic and Interventional Neuroradiology, Clinic for Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, 4031 Basel, Switzerland.
¹⁷ Department of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland.
¹⁸ Department of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
¹⁹ Department of Radiology, Faculty of Medicine, Geneva University Hospital, 1205 Geneva, Switzerland.
²⁰ Department of Neuroradiology, Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland.
²¹ Department of Radiology, Cantonal Hospital St. Gallen, 9000 St. Gallen, Switzerland.
²² Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland.
²³ Department of Neurology, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland.

Abstract

The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for CEL detection. This study aimed to develop a deep learning model that automatically detects and segments CELs in clinical Magnetic Resonance Imaging (MRI) scans. A 3D UNet-based network was trained with clinical MRI from the Swiss Multiple Sclerosis Cohort. The dataset comprised 372 scans from 280 MS patients: 162 showed at least one CEL, while 118 showed no CELs. The input dataset consisted of T1-weighted before and after gadolinium injection, and FLuid Attenuated Inversion Recovery images. The sampling strategy was based on a white matter lesion mask to confirm the existence of real contrast-enhancing lesions. To overcome the dataset imbalance, a weighted loss function was implemented. The Dice Score Coefficient and True Positive and False Positive Rates were 0.76, 0.93, and 0.02, respectively. Based on these results, the model developed in this study might well be considered for clinical decision support.

Keywords: automatic segmentation; deep learning; gadolinium contrast-enhancing lesions; multiple sclerosis.

Grants and funding

This research received no external funding.