Abstract #4850
Gadolinium contrast-enhanced lesion segmentation in multiple sclerosis: a deep-learning approach.
Martina Greselin1,2,3, Po-Jui Lu1,2,3, Lester Melie-Garcia1,2,3, Mario Ocampo-Pineda1,2,3, Riccardo Galbusera1,2,3, Alessandro Cagol1,2,3,4, Matthias Weigel1,2,3,5, Nina de Oliveira Siebenborn1,2,3,6, Esther Ruberte1,2,3,6, Pascal Benkert7, Stefanie Müller8, Lutz Achtnichts9, Jochen Vehoff8, Giulio Disanto10, Oliver Findling9, Andrew Chan11, Anke Salmen11,12, Caroline Pot13, Claire Bridel14, Chiara Zecca10,15, Tobias Derfuss3, Johanna M. Lieb16, Luca Remonda17, Franca Wagner18, Maria I. Vargas19, Renaud Du Pasquier13, Patrice H. Lalive14, Emanuele Pravatà20, Johannes Weber21, Claudio Gobbi10,15, David Leppert3, Ludwig Kappos1,2,3, Jens Kuhle2,3, and Cristina Granziera1,2,3
1Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland, 2Department of Neurology, University Hospital Basel, Basel, Switzerland, 3Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland, 4Department of Health Sciences, University of Genova, Genova, Italy, 5Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, 6Medical Image Analysis Center (MIAC), Basel, Switzerland, 7Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland, 8Department of Neurology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland, 9Department of Neurology, Cantonal Hospital Aarau, Aarau, Switzerland, 10Neurology Department, Neurocenter of Southern Switzerland, Lugano, Switzerland, 11Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland, 12Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany, 13Service of Neurology, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland, 14Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland, 15Faculty of biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland, 16Division of Diagnostic and Interventional Neuroradiology, Clinic for Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland, 17Department of Radiology, Cantonal Hospital Aarau, Aarau, Switzerland, 18Department of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland, 19Department of Radiology, Geneva University Hospital and Faculty of Medicine, Geneva, Switzerland, 20Department of Neuroradiology, Neurocenter of Southern Switzerland, Lugano, Switzerland, 21Department of Radiology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
Synopsis
Keywords: Diagnosis/Prediction, Segmentation
Motivation: Detection of contrast-enhanced lesions (CELs) is fundamental for the diagnosis and monitoring of Multiple Sclerosis (MS) patients. This task is time-consuming and variable in the clinical setting. However, only a few studies reported automatic approaches.
Goal(s): To develop a deep-learning tool to automatically detect and segment CELs in clinical MRI scans from MS patients.
Approach: We implemented a UNet-based network with an adapted sampling strategy to overcome the scarcity of CELs. We considered the data imbalance to weight the training loss function.
Results: The model performance was evaluated for different lesion-volume ranges and achieved high performance even in low-volume lesions.
Impact: We developed a deep-learning method fulfilling clinical needs in detecting and segmenting lesions characterized by low volume, low numbers per patient and heterogeneous shapes.
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