White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

Abstract

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.

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References 77

1. 1.

   SPIRIT. A randomized trial of anticoagulants versus aspirin after cerebral ischemia of presumed arterial origin. The Stroke Prevention in Reversible Ischemia Trial (SPIRIT) Study Group. Ann. Neurol. 1997;42(6):857-865.

   Not in our library10.1002/ana.410420606

2. 2.

   Al-Rfou R., Alain G., Almahairi A., Angermueller C., Bahdanau D., Ballas N., Bastien F., Bayer J., Belikov A., Belopolsky A., Bengio Y., Bergeron A., Bergstra J., Bisson V., Bleecher Snyder J., Bouchard N., Boulanger-Lewandowski N., Bouthillier X., de Brébisson A., Breuleux O., Carrier P.-L., Cho K., Chorowski J., Christiano P., Cooijmans T., Côté M.-A., Côté M., Courville A., Dauphin Y.N., Delalleau O., Demouth J., Desjardins G., Dieleman S., Dinh L., Ducoffe M., Dumoulin V., Ebrahimi Kahou S., Erhan D., Fan Z., Firat O., Germain M., Glorot X., Goodfellow I., Graham M., Gulcehre C., Hamel P., Harlouchet I., Heng J.-P., Hidasi B., Honari S., Jain A., Jean S., Jia K., Korobov M., Kulkarni V., Lamb A., Lamblin P., Larsen E., Laurent C., Lee S., Lefrancois S., Lemieux S., Léonard N., Lin Z., Livezey J.A., Lorenz C., Lowin J., Ma Q., Manzagol P.-A., Mastropietro O., McGibbon R.T., Memisevic R., van Merriënboer B., Michalski V., Mirza M., Orlandi A., Pal C., Pascanu R., Pezeshki M., Raffel C., Renshaw D., Rocklin M., Romero A., Roth M., Sadowski P., Salvatier J., Savard F., Schlüter J., Schulman J., Schwartz G., Serban I.V., Serdyuk D., Shabanian S., Simon E.´, Spieckermann S., Subramanyam S.R., Sygnowski J., Tanguay J., van Tulder G., Turian J., Urban S., Vincent P., Visin F., de Vries H., Warde-Farley D., Webb D.J., Willson M., Xu K., Xue L., Yao L., Zhang S., Zhang Y. Theano: A {Python} framework for fast computation of mathematical expressions. 2016.

   Not in our library

3. 3.

   Anbeek P., Vincken K.L., Van Osch M.J., Bisschops R.H., Van Der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage. 2004;21(3):1037-1044.

   Not in our library10.1016/j.neuroimage.2003.10.012

4. 4.

   Anbeek P., Vincken K.L., van Osch M.J., Bisschops R.H., van der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage. 2004;21(3):1037-1044.

   Not in our library10.1016/j.neuroimage.2003.10.012

5. 5.

   Bartko J.J. Measurement and reliability: statistical thinking considerations. Schizophr. Bull. 1991;17(3):483-489.

   Not in our library10.1093/schbul/17.3.483

6. 6.

   Bendfeldt K., Blumhagen J.O., Egger H., Loetscher P., Denier N., Kuster P., Traud S., Mueller-Lenke N., Naegelin Y., Gass A., Hirsch J., Kappos L., Nichols T.E., Radue E.-W., Borgwardt S.J. Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing-remitting multiple sclerosis. Hum. Brain Mapp. 2010;31(10):1542-1555.

   Not in our library10.1002/hbm.20951

7. 7.

   Birenbaum A., Greenspan H. Longitudinal multiple sclerosis lesion segmentation using multi-view convolutional neural networks. International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. 2016;58-67.

   Not in our library

8. 8.

   Bowles C., Qin C., Ledig C., Guerrero R., Hammers A., Sakka E., Dickie D.A., Tijms B., Lemstra A.W., Flier W.V.D. Pseudo-healthy image synthesis for white matter lesion segmentation. International Workshop on Simulation and Synthesis in Medical Imaging. 2016;vol. 9968:87-96.

   Not in our library

9. 9.

   Brosch T., Tang L.Y., Yoo Y., Li D.K., Traboulsee A., Tam R. Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging. 2016;35(5):1229-1239.

   Not in our library10.1109/tmi.2016.2528821

10. 10.

    Brosch T., Yoo Y., Tang L.Y., Li D.K., Traboulsee A., Tam R. Deep convolutional encoder networks for multiple sclerosis lesion segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2015;3-11.

    Not in our library

11. 11.

    Brott T., Marler J.R., Olinger C.P., Adams H.P., Tomsick T., Barsan W.G., Biller J., Eberle R., Hertzberg V., Walker M. Measurements of acute cerebral infarction: lesion size by computed tomography. Stroke. 1989;20(7):871-875.

    Not in our library10.1161/01.str.20.7.871

12. 12.

    Caligiuri M.E., Perrotta P., Augimeri A., Rocca F., Quattrone A., Cherubini A. Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: a review. Neuroinformatics. 2015;13(3):261-276.

    Not in our library10.1007/s12021-015-9260-y

13. 13.

    Cardoso M.J., Sudre C.H., Modat M., Ourselin S. Template-based multimodal joint generative model of brain data. Information Processing in Medical Imaging. 2015;vol. 9123:17-29.

    Not in our library10.1007/978-3-319-19992-4_2

14. 14.

    Chard D.T., Griffin C.M., Parker G.J.M., Kapoor R., Thompson A.J., Miller D.H. Brain atrophy in clinically early relapsing-remitting multiple sclerosis. Brain J. Neurol. 2002;125(2):327-337.

    Not in our library10.1093/brain/awf025

15. 15.

    Cohen J. Statistical Power Analysis for the Behavioral Sciences. 1988;40.

    Not in our library

16. 16.

    Dalca A.V., Sridharan R., Cloonan L., Fitzpatrick K.M., Kanakis A., Furie K.L., Rosand J., Wu O., Sabuncu M., Rost N.S. Segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors. Medical image computing and computer-assisted intervention: MICCAI. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014;vol. 17:773.

    Not in our library10.1007/978-3-319-10470-6_96

17. 17.

    Damangir S., Manzouri A., Oppedal K., Carlsson S., Firbank M.J., Sonnesyn H., Tysnes O.-B., O’Brien J.T., Beyer M.K., Westman E., Aarsland D., Wahlund L.-O., Spulber G. Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines. J. Neurol. Sci. 2012;322(1):211-216.

    Not in our library10.1016/j.jns.2012.07.064

18. 18.

    Derntl A., Plant C., Gruber P., Wegener S., Bauer J.S., Menze B.H. Stroke lesion segmentation using a probabilistic atlas of cerebral vascular territories. International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2015;21-32.

    Not in our library

19. 19.

    Dieleman S., Schlüter J., Raffel C., Olson E., Sønderby S.K., Nouri D., Maturana D., Thoma M., Battenberg E., Kelly J., Fauw J.D., Heilman M., de Almeida D.M., McFee B., Weideman H., Takács G., de Rivaz P., Crall J., Sanders G., Rasul K., Liu C., French G., Degrave J. Lasagne: First release. 2015.

    Not in our library

20. 20.

    Dong C., Loy C.C., He K., Tang X. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 2016;38(2):295-307.

    Not in our library10.1109/tpami.2015.2439281

21. 21.

    Dugas-Phocion G., Ballester M.A.G., Malandain G., Lebrun C., Ayache N. Improved em-based tissue segmentation and partial volume effect quantification in multi-sequence brain mri. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2004;26-33.

    Not in our library

22. 22.

    Fazekas F., Chawluk J.B., Alavi A., Hurtig H.I., Zimmerman R.A. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am. J. Roentgenol. 1987;149(2):351-356.

    Not in our library10.2214/ajr.149.2.351

23. 23.

    Forbes F., Doyle S., Garcia-Lorenzo D., Barillot C., Dojat M. Adaptive weighted fusion of multiple mr sequences for brain lesion segmentation. Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. 2010;69-72.

    Not in our library

24. 24.

    Freifeld O., Greenspan H., Goldberger J. Multiple sclerosis lesion detection using constrained GMM and curve evolution. J. Biomed. Imaging. 2009;2009:14.

    Not in our library10.1155/2009/715124

25. 25.

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

    Not in our library10.1016/j.media.2012.09.004

26. 26.

    Geremia E., Clatz O., Menze B.H., Konukoglu E., Criminisi A., Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage. 2011;57(2):378-390.

    Not in our library10.1016/j.neuroimage.2011.03.080

27. 27.
    Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities · 2017

    No notice
28. 28.

    Ghafoorian M., Karssemeijer N., Heskes T., van Uder I.W.M., de Leeuw F.E., Marchiori E., van Ginneken B., Platel B. Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. International Symposium on Biomedical Imaging. 2016, apr;1414-1417.

    Not in our library

29. 29.

    Gibson E., Gao F., Black S.E., Lobaugh N.J. Automatic segmentation of white matter hyperintensities in the elderly using flair images at 3t. J. Magn. Reson. Imaging. 2010;31(6):1311-1322.

    Not in our library10.1002/jmri.22004

30. 30.

    Girshick R., Donahue J., Darrell T., Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2014;580-587.

    Not in our library

31. 31.
    BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities · 2016

    No notice
32. 32.

    He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.

    Not in our library

33. 33.

    He K., Zhang X., Ren S., Sun J. Identity Mappings in Deep Residual Networks. 2016.

    Not in our library

34. 34.

    Hua K., Zhang J., Wakana S., Jiang H., Li X., Reich D.S., Calabresi P.A., Pekar J.J., van Zijl P.C., Mori S. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. NeuroImage. 2008;39(1):336-347.

    Not in our library10.1016/j.neuroimage.2007.07.053

35. 35.

    Ithapu V., Singh V., Lindner C., Austin B.P., Hinrichs C., Carlsson C.M., Bendlin B.B., Johnson S.C. Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies. Hum. Brain Mapp. 2014;35(8):219-4235.

    Not in our library10.1002/hbm.22472

36. 36.

    Itti L., Chang L., Ernst T. Segmentation of progressive multifocal leukoencephalopathy lesions in fluid-attenuated inversion recovery magnetic resonance imaging. J. Neuroimaging. 2001;11(4):412-417.

    Not in our library10.1111/j.1552-6569.2001.tb00071.x

37. 37.

    Jenkinson M., Bannister P., Brady M., Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage. 2002;17(2):825-841.

    Not in our library10.1016/s1053-8119(02)91132-8

38. 38.

    Kamnitsas K., Chen L., Ledig C., Rueckert D., Glocker B. Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. ISLES-MICCAI. 2015.

    Not in our library

39. 39.

    Kamnitsas K., Ledig C., Newcombe V.F., Simpson J.P., Kane A.D., Menon D.K., Rueckert D., Glocker B. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 2017;36:61-78.

    Not in our library10.1016/j.media.2016.10.004

40. 40.

    Kawata Y., Arimura H., Yamashita Y., Magome T., Ohki M., Toyofuku F., Higashida Y., Tsuchiya K. Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging. Comput. Med. Imaging Graph. 2010;34(5):370-376.

    Not in our library10.1016/j.compmedimag.2009.12.014

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• Deep Neural Architectures for Medical Image Semantic Segmentation: Review · 2021
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