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Longitudinal Patch-Based Segmentation of Multiple Sclerosis White Matter Lesions

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Machine Learning in Medical Imaging (MLMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9352))

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Abstract

Segmenting \(T_2\)-weighted white matter lesions from longitudinal MR images is essential in understanding progression of multiple sclerosis. Most lesion segmentation techniques find lesions independently at each time point, even though there are different noise and image contrast variations at each point in the time series. In this paper, we present a patch based 4D lesion segmentation method that takes advantage of the temporal component of longitudinal data. For each subject with multiple time-points, 4D patches are constructed from the \(T_1\)-w and FLAIR scans of all time-points. For every 4D patch from a subject, a few relevant matching 4D patches are found from a reference, such that their convex combination reconstructs the subject’s 4D patch. Then corresponding manual segmentation patches of the reference are combined in a similar manner to generate a 4D membership of lesions of the subject patch. We compare our 4D patch-based segmentation with independent 3D voxel-based and patch-based lesion segmentation algorithms. Based on ground truth segmentations from 30 data sets, we show that the mean Dice coefficients between manual and automated segmentations improve after using the 4D approach compared to two state-of-the-art 3D segmentation algorithms.

S. Roy—Support for this work included funding from the Department of Defense in the Center for Neuroscience and Regenerative Medicine and by the grants NIH/NINDS R01NS070906.

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References

  1. Garcia-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. 17(1), 1–18 (2013)

    Article  Google Scholar 

  2. 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 57(2), 378–390 (2011)

    Article  Google Scholar 

  3. Shiee, N., Bazin, P.L., Ozturk, A., Reich, D.S., Calabresi, P.A., Pham, D.L.: A Topology-Preserving Approach to the Segmentation of Brain Images with Multiple Sclerosis Lesions. NeuroImage 49(2), 1524–1535 (2009)

    Article  Google Scholar 

  4. Roy, S., He, Q., Carass, A., Jog, A., Cuzzocreo, J.L., Reich, D.S., Prince, J.L., Pham, D.L.: Example based lesion segmentation. In: Proc. of SPIE, vol. 9034, p. 90341Y (2014)

    Google Scholar 

  5. Ganiler, O., Oliver, A., Diez, Y., Freixenet, J., Vilanova, J.C., Beltran, B., Ramio-Torrenta, L., Rovira, A., Llado, X.: A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies. Neuroradiology 56(5), 363–374 (2014)

    Article  Google Scholar 

  6. Gerig, G., Welti, D., Guttmann, C.R.G., Colchester, A.C.F., Szekely, G.: Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data. Med. Image Anal. 4(1), 31–42 (2000)

    Article  Google Scholar 

  7. Roy, S., Carass, A., Prince, J.L.: Magnetic resonance image example based contrast synthesis. IEEE Trans. Med. Imag. 32(12), 2348–2363 (2013)

    Article  Google Scholar 

  8. Roy, S., Carass, A., Prince, J.L.: Synthesizing MR contrast and resolution through a patch matching technique. In: Proc. of SPIE, vol. 7263, p. 76230j (2010)

    Google Scholar 

  9. Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D.: Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage 16(1), 152–164 (2014)

    Article  Google Scholar 

  10. Weiss, N., Rueckert, D., Rao, A.: Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 735–742. Springer, Heidelberg (2013)

    Google Scholar 

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Correspondence to Snehashis Roy .

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Roy, S., Carass, A., Prince, J.L., Pham, D.L. (2015). Longitudinal Patch-Based Segmentation of Multiple Sclerosis White Matter Lesions. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-24888-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24887-5

  • Online ISBN: 978-3-319-24888-2

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