Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE

NeuroImage: Clinical

Cristina Granziera¹  Riccardo Galbusera²  Reza Rahmanzadeh³  Mário João Fartaria⁴  Jean-Philippe Thiran⁴  Ahmed Abdulkadir⁵  Muhamed Barakovic⁶  Francesco La Rosa⁶  Merixtell Bach Cuadra⁷  Po-Jui Lu⁸ 

[1] Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland;Center for Biomedical Image Computing and Analytics at the Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States;Corresponding author at: Signal Processing Laboratory (LTS5), EPFL-STI-IEL-LTS5 Station 11, CH-1015 Lausanne, Switzerland.;Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland;Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland;Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland;Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland;University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland;

关键词: MRI;    Multiple sclerosis;    Cortical lesions;    Segmentation;    CNN;    U-Net;   

DOI  :  

来源: DOAJ

【 摘 要 】

The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners.

【 授权许可】

Unknown