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Neuroradiology

Erschienen in:

02.10.2021 | Diagnostic Neuroradiology

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation

verfasst von: Yajing Zhang, Yunyun Duan, Xiaoyang Wang, Zhizheng Zhuo, Sven Haller, Frederik Barkhof, Yaou Liu

Erschienen in: Neuroradiology | Ausgabe 4/2022

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Abstract

Purpose

White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types.

Methods

We developed and evaluated “DeepWML”, a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard).

Results

The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool’s performance increased with larger lesion volumes.

Conclusion

DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation.

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Rovira A, León A (2008) MR in the diagnosis and monitoring of multiple sclerosis: an overview. Eur J Radiol 67(3):409–414CrossRef

Wattjes MP, Rovira À, Miller D, Yousry TA, Sormani MP, de Stefano MP et al (2015) Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis–establishing disease prognosis and monitoring patients. Nat Rev Neurol 11(10):597–606PubMed

Asgari N, Skejoe HPB, Lillevang ST, Steenstrup T, Stenager E, Kyvik KO (2013) Modifications of longitudinally extensive transverse myelitis and brainstem lesions in the course of neuromyelitis optica (NMO): a population-based, descriptive study. BMC Neurol 13:33–33CrossRef

Dutra BG, da Rocha AJ, Nunes RH, Maia ACMJ (2018) Neuromyelitis optica spectrum disorders: spectrum of MR imaging findings and their differential diagnosis. Radiographics : a review publication of the Radiological Society of North America, Inc. 38(1): p. 169–193

Zhang X, Tang Y, Xie Y, Ding C, Xiao J, Jiang X et al (2017) Total magnetic resonance imaging burden of cerebral small-vessel disease is associated with post-stroke depression in patients with acute lacunar stroke. Eur J Neurol 24(2):374–380CrossRef

Li G, Zhu C, Li J, Wang X, Zhang Q, Zheng H et al (2018) Increased level of procalcitonin is associated with total MRI burden of cerebral small vessel disease in patients with ischemic stroke. Neurosci Lett 662:242–246CrossRef

McKinley R, Wepfer R, Grunder L, Aschwanden F, Fischer T, Friedli C et al (2019) Automatic detection of lesion load change in multiple sclerosis using convolutional neural networks with segmentation confidence. NeuroImage Clinical 25:102104–102104CrossRef

Salem M, Valverde S, Cabezas M, Pareto D, Oliver A, Salvi J et al (2019) A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis. NeuroImage Clinical 25:102149–102149CrossRef

Zijdenbos AP, Forghani R, Evans AC (2002) Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21(10):1280–1291CrossRef

10.

García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL (2013) Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal 17(1):1–18CrossRef

11.

Dadar M, Maranzano J, Misquitta K, Anor CJ, Fonov VS, Tartaglia MC, et al. Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging. (1095–9572 (Electronic)).

12.

Diniz PHB, Valente TLA, Diniz JOB, Silva AC, Gattass M, Ventura N et al (2018) Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network. Comput Methods Programs Biomed 167:49–63CrossRef

13.

Li H, Jiang G, Zhang J, Wang R, Wang Z, Zheng W-S et al (2018) Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images. Neuroimage 183:650–665CrossRef

14.

Xu B, Chai Y, Galarza CM, Vu CQ, Tamrazi B, Gaonkar B et al (2018) Orchestral fully convolutional networks for small lesion segmentation in brain MRI. . Proceedings IEEE International Symposium on Biomedical Imaging 2018:889–892PubMedPubMedCentral

15.

Duong MT, Rudie JD, Wang J, Xie L, Mohan S, Gee JC et al (2019) Convolutional neural network for automated FLAIR lesion segmentation on clinical brain MR imaging. AJNR Am J Neuroradiol 40(8):1282–1290CrossRef

16.

Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A et al (2012) An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59(4):3774–3783CrossRef

17.

Rachmadi MF, Valdés-Hernández MdC, Agan MLF, Di Perri C, Komura T (2018) Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology. Computerized Medical Imaging and Graphics. 66: p. 28-43

18.

La Rosa F, Abdulkadir A, Fartaria MJ, Rahmanzadeh R, P.-J. Lu, R. Galbusera, et al. (2020). Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE. NeuroImage: Clinical. 27: p. 102335

19.

Mongan JA-O, Moy LA-O, Kahn CEJA-O. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. (2638–6100 (Electronic))

20.

Heinen R, Steenwijk MD, Barkhof F, Biesbroek JM, van der Flier WM, Kuijf HJ et al (2019) Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset. Sci Rep 9(1):16742–16742CrossRef

21.

Le M, Tang LYW, Hernández-Torres E, Jarrett M, Brosch T, Metz L et al (2019) FLAIR(2) improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images. NeuroImage Clinical 23:101918–101918CrossRef

22.

Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. The Lancet Neurology 17(2):162–173CrossRef

23.

Wingerchuk DM, Banwell B, Bennett JL, Cabre P, Carroll W, Chitnis T et al (2015) International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 85(2):177–189CrossRef

24.

Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R et al (2013) Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology 12(8):822–838CrossRef

25.

Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30(9):1323–1341CrossRef

26.

Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham: Springer International Publishing

27.

Sudre CH, Li W, Vercauteren T, Ourselin S, Cardoso MJ (2017) Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer International Publishing

28.

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv e-prints: p. arXiv:1412.6980

29.

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv e-prints: p. arXiv:1912.01703

30.

Ribaldi F, Altomare D, Jovicich J, Ferrari C, Picco A, Pizzini FB et al (2021) Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study. Magn Reson Imaging 76:108–115CrossRef

31.

Steenwijk MD, Pouwels PJW, Daams M, van Dalen JW, Caan MWA, Richard E et al (2013) Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). NeuroImage Clinical 3:462–469CrossRef

32.

Damangir S, Westman E, Simmons A, Vrenken H, Wahlund L-O, Spulber G (2017) Reproducible segmentation of white matter hyperintensities using a new statistical definition. Magma (New York, N.Y.). 30(3): p. 227–237

Titel: A deep learning algorithm for white matter hyperintensity lesion detection and segmentation
verfasst von: Yajing Zhang
Yunyun Duan
Xiaoyang Wang
Zhizheng Zhuo
Sven Haller
Frederik Barkhof
Yaou Liu
Publikationsdatum: 02.10.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Neuroradiology / Ausgabe 4/2022
Print ISSN: 0028-3940
Elektronische ISSN: 1432-1920
DOI: https://doi.org/10.1007/s00234-021-02820-w

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Abstract

Purpose

Methods

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Conclusion

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