Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs. (September 2020)

• Record Type: Journal Article
• Authors: Gessert, Nils; Krüger, Julia; Opfer, Roland; Ostwaldt, Ann-Christin; Manogaran, Praveena; Kitzler, Hagen H.; Schippling, Sven; Schlaefer, Alexander
• Is Part Of: Computerized medical imaging and graphics. Volume 84(2020)
• Publication Date: 2020-09
• Subjects: Multiple sclerosis -- Lesion activity -- Segmentation -- Deep learning -- Attention; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101772
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Multiple sclerosis is an inflammatory autoimmune demyelinating disease that is characterized by lesions in the central nervous system. Typically, magnetic resonance imaging (MRI) is used for tracking disease progression. Automatic image processing methods can be used to segment lesions and derive quantitative lesion parameters. So far, methods have focused on lesion segmentation for individual MRI scans. However, for monitoring disease progression, lesion activity in terms of new and enlarging lesions between two time points is a crucial biomarker. For this problem, several classic methods have been proposed, e.g., using difference volumes. Despite their success for single-volume lesion segmentation, deep learning approaches are still rare for lesion activity segmentation. In this work, convolutional neural networks (CNNs) are studied for lesion activity segmentation from two time points. For this task, CNNs are designed and evaluated that combine the information from two points in different ways. In particular, two-path architectures with attention-guided interactions are proposed that enable effective information exchange between the two time point's processing paths. It is demonstrated that deep learning-based methods outperform classic approaches and it is shown that attention-guided interactions significantly improve performance. Furthermore, the attention modules produce plausible attention maps that have a masking effect that suppresses old, irrelevant lesions. A lesion-wise false positive rate of 26.4 % is achieved at a true positive rate of 74.2 %, which is not significantly different from the interrater performance. Highlights: Deep learning for multiple sclerosis lesion activity segmentation is investigated. Deep learning methods significantly outperform classic approaches. Two-path CNNs with attention-guided information exchange improve performance. Attention maps suppress old lesion regions, highlighting new and enlarged lesions.