Evaluating severity of white matter lesions from computed tomography images with convolutional neural network

Pitkanen J, Koikkalainen J, Nieminen T, Marinkovic I, Curtze S, Sibolt G, Jokinen H, Rueckert D, Barkhof F, Schmidt R, Pantoni L, Scheltens P, Wahlund LO, Korvenoja A, Lotjonen J, Erkinjuntti T, Melkas S (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Neuroradiology Springer Verlag (Germany)

Book Volume: 62

Pages Range: 1257-1263

Journal Issue: 10

DOI: 10.1007/s00234-020-02410-2

Abstract

Purpose: Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods: The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. Results: A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. Conclusion: CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale.

Involved external institutions

Helsingin yliopisto / University of Helsinki FI Finland (FI) Combinostics Oy FI Finland (FI) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Vrije Universiteit Amsterdam (VU) / University Amsterdam NL Netherlands (NL) Università degli studi di Milano IT Italy (IT) Karolinska Institute SE Sweden (SE) Helsinki University Central Hospital (HUCH) / Helsingin seudun yliopistollinen keskussairaala (HYKS) FI Finland (FI)

How to cite

APA:

Pitkanen, J., Koikkalainen, J., Nieminen, T., Marinkovic, I., Curtze, S., Sibolt, G.,... Melkas, S. (2020). Evaluating severity of white matter lesions from computed tomography images with convolutional neural network. Neuroradiology, 62(10), 1257-1263. https://doi.org/10.1007/s00234-020-02410-2

MLA:

Pitkanen, Johanna, et al. "Evaluating severity of white matter lesions from computed tomography images with convolutional neural network." Neuroradiology 62.10 (2020): 1257-1263.

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