Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review.
Lesion segmentation
Longitudinal
MRI
Multiple sclerosis
Review
Journal
Neuroradiology
ISSN: 1432-1920
Titre abrégé: Neuroradiology
Pays: Germany
ID NLM: 1302751
Informations de publication
Date de publication:
Nov 2022
Nov 2022
Historique:
received:
29
03
2022
accepted:
12
07
2022
pubmed:
22
7
2022
medline:
20
10
2022
entrez:
21
7
2022
Statut:
ppublish
Résumé
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these lesions can provide imaging biomarkers of disease burden that can help monitor disease progression and the imaging response to treatment. Manual delineation of MRI lesions is tedious and prone to subjective bias, while automated lesion segmentation methods offer objectivity and speed, the latter being particularly important when analysing large datasets. Lesion segmentation can be broadly categorised into two groups: cross-sectional methods, which use imaging data acquired at a single time-point to characterise MRI lesions; and longitudinal methods, which use imaging data from the same subject acquired at two or more different time-points to characterise lesions over time. The main objective of longitudinal segmentation approaches is to more accurately detect the presence of new MS lesions and the growth or remission of existing lesions, which may be effective biomarkers of disease progression and treatment response. This paper reviews articles on longitudinal MS lesion segmentation methods published over the past 10 years. These are divided into traditional machine learning methods and deep learning techniques. PubMed articles using longitudinal information and comparing fully automatic two time point segmentations in any step of the process were selected. Nineteen articles were reviewed. There is an increasing number of deep learning techniques for longitudinal MS lesion segmentation that are promising to help better understand disease progression.
Identifiants
pubmed: 35864180
doi: 10.1007/s00234-022-03019-3
pii: 10.1007/s00234-022-03019-3
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2103-2117Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Igra MS, Paling D, Wattjes MP et al (2017) Multiple sclerosis update: use of MRI for early diagnosis, disease monitoring and assessment of treatment related complications. Br J Radiol 90:20160721
pubmed: 28362522
pmcid: 5602172
doi: 10.1259/bjr.20160721
Thompson AJ, Banwell BL, Barkhof F et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17:162–173
pubmed: 29275977
doi: 10.1016/S1474-4422(17)30470-2
Rovira À, on behalf of the MAGNIMS study group, Wattjes MP et al (2015) MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—clinical implementation in the diagnostic process. Nat Rev Neuro 11:471–482
doi: 10.1038/nrneurol.2015.106
Molyneux PD (1998) Precision and reliability for measurement of change in MRI lesion volume in multiple sclerosis: a comparison of two computer assisted techniques. J Neurol Neurosurg Psychiatry 65:42–47
pubmed: 9667559
pmcid: 2170149
doi: 10.1136/jnnp.65.1.42
Sweeney EM, Shinohara RT, Shea CD et al (2013) Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI. AJNR Am J Neuroradiol 34:68–73
pubmed: 22766673
pmcid: 3554794
doi: 10.3174/ajnr.A3172
Calvi A, Haider L, Prados F, et al (2020) In vivo imaging of chronic active lesions in multiple sclerosis. Mult Scler 1352458520958589
Sethi V, Nair G, Absinta M et al (2017) Slowly eroding lesions in multiple sclerosis. Mult Scler 23:464–472
pubmed: 27339071
doi: 10.1177/1352458516655403
Miller DH (1994) Magnetic resonance in monitoring the treatment of multiple sclerosis. Ann Neurol 36(Suppl):S91–S94
pubmed: 8017895
doi: 10.1002/ana.410360720
Weeda MM, Brouwer I, de Vos ML, et al (2019) Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation. NeuroImage: Clin 24 https://doi.org/10.1016/j.nicl.2019.102074
Valverde S, Cabezas M, Roura E et al (2017) Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Neuroimage 155:159–168
pubmed: 28435096
doi: 10.1016/j.neuroimage.2017.04.034
Gros C, de Leener B, Badji A et al (2019) Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage 184:901–915
pubmed: 30300751
doi: 10.1016/j.neuroimage.2018.09.081
Carass A, Roy S, Jog A et al (2017) Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. Neuroimage 148:77–102
pubmed: 28087490
doi: 10.1016/j.neuroimage.2016.12.064
Cerri S, Puonti O, Meier DS et al (2021) A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis. Neuroimage 225:117471
pubmed: 33099007
doi: 10.1016/j.neuroimage.2020.117471
Ganiler O, Oliver A, Diez Y et al (2014) A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies. Neuroradiology 56:363–374
pubmed: 24590302
doi: 10.1007/s00234-014-1343-1
Jain S, Ribbens A, Sima DM et al (2016) Two time point MS lesion segmentation in brain MRI: an expectation-maximization framework. Front Neurosci 10:1–11
doi: 10.3389/fnins.2016.00576
Salem M, Cabezas M, Valverde S et al (2018) A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis. NeuroImage: Clin 17:607–615
doi: 10.1016/j.nicl.2017.11.015
Horsfield MA, Sala S, Neema M et al (2010) Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. Neuroimage 50:446–455
pubmed: 20060481
doi: 10.1016/j.neuroimage.2009.12.121
Hickman SJ (2007) Optic nerve imaging in multiple sclerosis. J Neuroimaging 17(Suppl 1):42S-45S
pubmed: 17425734
doi: 10.1111/j.1552-6569.2007.00136.x
Doyle A, Elliott C, Karimaghaloo Z, et al (2018) Lesion detection, segmentation and prediction in multiple sclerosis clinical trials. Brainlesion: Glioma, Mult Scler, Stroke Trauma Brain Inj 15–28
Lladó X, Ganiler O, Oliver A et al (2012) Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology 54:787–807
pubmed: 22179659
doi: 10.1007/s00234-011-0992-6
Juntu J, Sijbers J, Dyck D, Gielen J (2008) Bias field correction for MRI images. Advances in Soft Computing. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 543–551
Tustison N, Gee J (2010) N4ITK: Nick’s N3 ITK implementation for MRI bias field correction. The Insight J https://doi.org/10.54294/jculxw
Freire PGL, Ferrari RJ (2016) Automatic iterative segmentation of multiple sclerosis lesions using Student’s t mixture models and probabilistic anatomical atlases in FLAIR images. Comput Biol Med 73:10–23
pubmed: 27058437
doi: 10.1016/j.compbiomed.2016.03.025
Leung KK, Ridgway GR, Ourselin S et al (2012) Consistent multi-time-point brain atrophy estimation from the boundary shift integral. Neuroimage 59:3995–4005
pubmed: 22056457
doi: 10.1016/j.neuroimage.2011.10.068
Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61:1402–1418
pubmed: 22430496
doi: 10.1016/j.neuroimage.2012.02.084
Alexa M (2002) Linear combination of transformations. ACM Trans Graph 21:380–387
doi: 10.1145/566654.566592
Brett M, Johnsrude IS, Owen AM (2002) The problem of functional localization in the human brain. Nat Rev Neurosci 3:243–249
pubmed: 11994756
doi: 10.1038/nrn756
Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155
pubmed: 12391568
pmcid: 6871816
doi: 10.1002/hbm.10062
Eskildsen SF, Coupé P, Fonov V et al (2012) BEaST: brain extraction based on nonlocal segmentation technique. Neuroimage 59:2362–2373
pubmed: 21945694
doi: 10.1016/j.neuroimage.2011.09.012
Iglesias JE, Liu C-Y, Thompson PM, Tu Z (2011) Robust brain extraction across datassets and comparison with publicly available methods. IEEE Trans Med Imaging 30:1617–1634
pubmed: 21880566
doi: 10.1109/TMI.2011.2138152
Isensee F, Schell M, Pflueger I et al (2019) Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp 40:4952–4964
pubmed: 31403237
pmcid: 6865732
doi: 10.1002/hbm.24750
Bosc M, Heitz F, Armspach JP et al (2003) Automatic change detection in multimodal serial MRI: application to multiple sclerosis lesion evolution. Neuroimage 20:643–656
pubmed: 14568441
doi: 10.1016/S1053-8119(03)00406-3
Commowick O, Istace A, Kain M et al (2018) Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure. Sci Rep 8:13650
pubmed: 30209345
pmcid: 6135867
doi: 10.1038/s41598-018-31911-7
Lesjak Ž, Pernuš F, Likar B, Špiclin Ž (2016) Validation of white-matter lesion change detection methods on a novel publicly available MRI image Database. Neuroinformatics 14:403–420
pubmed: 27207310
doi: 10.1007/s12021-016-9301-1
Mori S, Oishi K, Jiang H et al (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40:570–582
pubmed: 18255316
doi: 10.1016/j.neuroimage.2007.12.035
Oishi K, Zilles K, Amunts K et al (2008) Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage 43:447–457
pubmed: 18692144
doi: 10.1016/j.neuroimage.2008.07.009
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156
pubmed: 11516708
doi: 10.1016/S1361-8415(01)00036-6
Sled JG, Bruce Pike G (1998) Understanding intensity non-uniformity in MRI. Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 614–622
Elliott C, Arnold DL, Collins DL, Arbel T (2013) Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI. IEEE Trans Med Imaging 32:1490–1503
pubmed: 23613032
doi: 10.1109/TMI.2013.2258403
Battaglini M, Rossi F, Grove RA et al (2014) Automated identification of brain new lesions in multiple sclerosis using subtraction images. J Magn Reson Imaging 39:1543–1549
pubmed: 24987754
doi: 10.1002/jmri.24293
Roy S, Carass A, Prince JL, Pham DL (2015) Longitudinal patch-based segmentation of multiple sclerosis white matter lesions. Machine Learning for Medical Imaging 9352:194–202
doi: 10.1007/978-3-319-24888-2_24
Simões R, Slump C (2011) Change detection and classification in brain MR images using change vector analysis. Proceedings of the Annual International Conference of the IEEE Eng Med Bio Soc EMBS 7803–7807
Cabezas M, Corral JF, Oliver A et al (2016) Improved automatic detection of new t2 lesions in multiple sclerosis using deformation fields. AJNR Am J Neuroradiol 37:1816–1823
pubmed: 27282863
pmcid: 7960461
doi: 10.3174/ajnr.A4829
Schmidt P, Pongratz V, Küster P et al (2019) Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging. NeuroImage: Clin 23:101849
doi: 10.1016/j.nicl.2019.101849
Birenbaum A, Greenspan H (2017) Multi-view longitudinal CNN for multiple sclerosis lesion segmentation. Eng Appl Artif Intell 65:111–118
doi: 10.1016/j.engappai.2017.06.006
Fartaria MJ, Kober T, Granziera C, Bach Cuadra M (2019) Longitudinal analysis of white matter and cortical lesions in multiple sclerosis. NeuroImage: Clin 23:101938
doi: 10.1016/j.nicl.2019.101938
Salem M, Valverde S, Cabezas M, et al (2020) A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis. NeuroImage: Clin 25 https://doi.org/10.1016/j.nicl.2019.102149
Balakrishnan G, Zhao A, Sabuncu MR et al (2019) VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging 38:1788–1800
doi: 10.1109/TMI.2019.2897538
Denner S, Khakzar A, Sajid M, et al (2020) Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation. In: BrainLes Workshop in MICCAI2020
Zhang H, Valcarcel AM, Bakshi R et al (2019) Multiple sclerosis lesion segmentation with tiramisu and 2.5D stacked slices. Med Image Comput Comput Assist Interv 11766:338–346
pubmed: 34950934
pmcid: 8692167
Krüger J, Opfer R, Gessert N et al (2020) Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks. NeuroImage: Clin 28:102445
doi: 10.1016/j.nicl.2020.102445
McKinley R, Wepfer R, Grunder L et al (2020) Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. NeuroImage: Clin 25:102104
doi: 10.1016/j.nicl.2019.102104
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp 234–241
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2016) Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Gessert N, Krüger J, Opfer R et al (2020) Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs. Comput Med Imaging Graph 84:101772
pubmed: 32795845
doi: 10.1016/j.compmedimag.2020.101772
Giovannoni G, Turner B, Gnanapavan S et al (2015) Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult Scler Relat Disord 4:329–333
pubmed: 26195051
doi: 10.1016/j.msard.2015.04.006
Prados F, Cardoso MJ, Kanber B et al (2016) A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis. Neuroimage 139:376–384
pubmed: 27377222
doi: 10.1016/j.neuroimage.2016.06.053
Battaglini M, Jenkinson M, De Stefano N (2012) Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 33:2062–2071
pubmed: 21882300
doi: 10.1002/hbm.21344
Akkus Z, Galimzianova A, Hoogi A et al (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30:449–459
pubmed: 28577131
pmcid: 5537095
doi: 10.1007/s10278-017-9983-4
Hashemi SR (2017) Asymmetric loss functions and deep densely connected networks for highly imbalanced medical image segmentation: application to multiple sclerosis lesion detection. Physiol Behav 176:139–148
Goodkin O, Prados F, Vos SB et al (2021) FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis. Neuroimage Clin 29:102542
pubmed: 33418171
doi: 10.1016/j.nicl.2020.102542
Pemberton HG, Goodkin O, Prados F et al (2021) Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study. Eur Radiol 31:5312–5323
pubmed: 33452627
pmcid: 8213665
doi: 10.1007/s00330-020-07455-8