Abstract
Automatic segmentation of chronic stroke lesion from magnetic resonance images (MRI) is motivated by the increasing need for reproducible and repeatable endpoints in clinical trials. The task is non-trivial, due to a number of confounding factors, including heterogeneous lesion intensity, irregular shape, and large deformations that render the conventional use of prior probabilistic atlases challenging. In this paper, we introduce a hidden Markov random field model that avails of a novel prior probabilistic vascular territory atlas to describe the natural vascular constraints in the brain. The vascular territory atlas is deformed in a joint registration-segmentation framework to overcome subject-specific morphological variability. T1-w and Flair sequences are used to populate our model, and a variational approach is implemented to find a solution. The performance of our model is demonstrated on two datasets, and compared to manual delineations by expert raters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The pre-processed data, ground truth and segmentation results are available at http://dx.doi.org/10.6084/m9.figshare.1585018.
- 2.
- 3.
- 4.
References
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)
Chalela, J.A., Kidwell, C.S., Nentwich, L.M., Luby, M., Butman, J.A., Demchuk, A.M., Hill, M.D., Patronas, N., Latour, L., Warach, S.: Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet 369(9558), 293–298 (2007)
Fiez, J.A., Damasio, H., Grabowski, T.J.: Lesion segmentation and manual warping to a reference brain: intra- and interobserver reliability. Hum. Brain Mapp. 9(4), 192–211 (2000)
Forbes, F., Doyle, S., Garcia-Lorenzo, D., Barillot, C., Dojat, M.: A weighted multi-sequence markov model for brain lesion segmentation. In: Whye Teh, Y., Titterington, M. (eds.) Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (2010)
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, 2nd edn. Chapman and Hall/CRC, Boca Raton (2004)
Griffis, J.C., Allendorfer, J.B., Szaflarski, J.P.: Voxel-based gaussian naive bayes classification of ischemic stroke lesions in individual t1-weighted MRI scans. J. Neurosci. Methods 257, 97–108 (2016). https://www.ncbi.nlm.nih.gov/pubmed/26432931
de Haan, B., Clas, P., Juenger, H., Wilke, M., Karnath, H.O.: Fast semi-automated lesion demarcation in stroke. Neuroimage Clin. 9, 69–74 (2015). https://www.ncbi.nlm.nih.gov/pubmed/26413473
Hommel, M., Miguel, S.T., Naegele, B., Gonnet, N., Jaillard, A.: Cognitive determinants of social functioning after a first ever mild to moderate stroke at vocational age. J. Neurol. Neurosurg. Psychiatry 80(8), 876–880 (2009), Epub 8 Apr 2009. https://doi.org/10.1136/jnnp.2008.169672
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. 36, 61–78 (2017)
Luby, M., Bykowski, J.L., Schellinger, P.D., Merino, J.G., Warach, S.: Intra- and interrater reliability of ischemic lesion volume measurements on diffusion-weighted, mean transit time and fluid-attenuated inversion recovery MRI. Stroke 37(12), 2951–2956 (2006). https://www.ncbi.nlm.nih.gov/pubmed/17082470
Maier, O., Menze, B.H., von der Gablentz, J., Hani, L., Heinrich, M.P., Liebrand, M., Winzeck, S., Basit, A., Bentley, P., Chen, L., Christiaens, D., Dutil, F., Egger, K., Feng, C., Glocker, B., Gotz, M., Haeck, T., Halme, H.L., Havaei, M., Iftekharuddin, K.M., Jodoin, P.M., Kamnitsas, K., Kellner, E., Korvenoja, A., Larochelle, H., Ledig, C., Lee, J.H., Maes, F., Mahmood, Q., Maier-Hein, K.H., McKinley, R., Muschelli, J., Pal, C., Pei, L., Rangarajan, J.R., Reza, S.M., Robben, D., Rueckert, D., Salli, E., Suetens, P., Wang, C.W., Wilms, M., Kirschke, J.S., Kramer, U.M., Munte, T.F., Schramm, P., Wiest, R., Handels, H., Reyes, M.: Isles 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)
Maier, O., Schroder, C., Forkert, N.D., Martinetz, T., Handels, H.: Classifiers for ischemic stroke lesion segmentation: a comparison study. PLoS ONE 10(12), e0145118 (2015)
Maier, O., Wilms, M., von der Gablentz, J., Kramer, U.M., Munte, T.F., Handels, H.: Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J. Neurosci. Methods 240, 89–100 (2015)
Menze, B.H., Van Leemput, K., Lashkari, D., Riklin-Raviv, T., Geremia, E., Alberts, E., Gruber, P., Wegener, S., Weber, M.A., Szekely, G., Ayache, N., Golland, P.: A generative probabilistic model and discriminative extensions for brain lesion segmentation-with application to tumor and stroke. IEEE Trans. Med. Imaging 35(4), 933–946 (2016)
Mitra, J., Bourgeat, P., Fripp, J., Ghose, S., Rose, S., Salvado, O., Connelly, A., Campbell, B., Palmer, S., Sharma, G., Christensen, S., Carey, L.: Lesion segmentation from multimodal MRI using random forest following ischemic stroke. Neuroimage 98, 324–335 (2014)
Morey, R.A., Selgrade, E.S., Wagner, H. R., n., Huettel, S.A., Wang, L., McCarthy, G.: Scan-rescan reliability of subcortical brain volumes derived from automated segmentation. Hum. Brain Mapp. 31(11), 1751–1762 (2010)
Neumann, A.B., Jonsdottir, K.Y., Mouridsen, K., Hjort, N., Gyldensted, C., Bizzi, A., Fiehler, J., Gasparotti, R., Gillard, J.H., Hermier, M., Kucinski, T., Larsson, E.M., Sorensen, L., Ostergaard, L.: Interrater agreement for final infarct mri lesion delineation. Stroke 40(12), 3768–3771 (2009)
Pustina, D., Coslett, H.B., Turkeltaub, P.E., Tustison, N., Schwartz, M.F., Avants, B.: Automated segmentation of chronic stroke lesions using linda: Lesion identification with neighborhood data analysis. Hum. Brain Mapp. 37(4), 1405–1421 (2016). https://www.ncbi.nlm.nih.gov/pubmed/26756101
Ritzl, A., Meisel, S., Wittsack, H.J., Fink, G.R., Siebler, M., Modder, U., Seitz, R.J.: Development of brain infarct volume as assessed by magnetic resonance imaging (MRI): follow-up of diffusion-weighted MRI lesions. J. Magn. Reson. Imaging 20(2), 201–207 (2004). https://www.ncbi.nlm.nih.gov/pubmed/15269944
Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: A joint Bayesian framework for MR brain scan tissue and structure segmentation based on distributed Markovian agents. In: Bichindaritz, I., Vaidya, S., Jain, A., Jain, L.C. (eds.) Computational Intelligence in Healthcare 4. SCI, vol. 309, pp. 81–101. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14464-6_5
Seghier, M.L., Ramlackhansingh, A., Crinion, J., Leff, A.P., Price, C.J.: Lesion identification using unified segmentation-normalisation models and fuzzy clustering. Neuroimage 41(4), 1253–1266 (2008)
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4itk: improved N3 bias correction. IEEE Trans. Med. Imaging. 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908. Epub 8 Apr 2010 (2010)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction in MR images of the brain. IEEE trans. Med. Imag. 18(10), 885–896 (1999)
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903–921 (2004)
Wilke, M., de Haan, B., Juenger, H., Karnath, H.O.: Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. Neuroimage 56(4), 2038–2046 (2011)
Acknowledgments
This work was partly supported by ‘RESSTORE’ project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 681044. Grenoble MRI facility IRMaGe was partly funded by the French program Investissement d’avenir run by the Agence Nationale pour la Recherche; grant Infrastructure d’avenir en Biologie Santé - ANR-11-INBS-0006.
The authors would also like to thank Oskar Maier and his colleagues for supplying dataset A.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Doyle, S., Forbes, F., Jaillard, A., Heck, O., Detante, O., Dojat, M. (2018). Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-75238-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75237-2
Online ISBN: 978-3-319-75238-9
eBook Packages: Computer ScienceComputer Science (R0)