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A Fast Approach to Automatic Detection of Brain Lesions

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Book cover Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10154))

Abstract

Template matching is a popular approach to computer-aided detection of brain lesions from magnetic resonance (MR) images. The outcomes are often sufficient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates is demanding in terms of computational resources, hence the importance of the reduction of computational complexity of template matching, particularly in situations in which time is crucial (e.g. emergent stroke). In view of this, we make use of 3D Gaussian templates with varying radii and propose a new method to compute the normalized cross-correlation coefficient as a similarity metric between the MR volume and the template to detect brain lesions. Contrary to the conventional fast Fourier transform (FFT) based approach, whose runtime grows as \( O\left( {N\,\log N} \right) \) with the number of voxels, the proposed method computes the cross-correlation in \( O\left( N \right) \). We show through our experiments that the proposed method outperforms the FFT approach in terms of computational time, and retains comparable accuracy.

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Notes

  1. 1.

    We ran the experiments on a Linux computing cluster. Although we used MATLAB with the singleCompThread option, it still multithreaded the codes on multiple cores. Therefore, here for each experiment we report the total time spent by the cores (CPU time), as opposed to the real-world time elapsed between the start and end times of the code (wall time).

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Acknowledgments

Support for this research was provided in part by the National Institute of Diabetes and Digestive and Kidney Diseases (K01DK101631, R21DK108277), the National Institute for Biomedical Imaging and Bioengineering (P41EB015896, R01EB006758, R21EB018907, R01EB019956), the National Institute on Aging (5R01AG008122, R01AG016495), the National Institute for Neurological Disorders and Stroke (R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625), the National Institutes of Health (NIH) Blueprint for Neuroscience Research (5U01-MH093765; part of the multi-institutional Human Connectome Project), and was made possible by the resources provided by the NIH Shared Instrumentation Grants (S10RR023401, S10RR019307, and S10RR023043). Additionally, SK was supported by a Fulbright-Nehru Doctoral Research Fellowship (Award no: 2098/DR/2015-2016), and CC was supported by the DAE-Young Scientist Research Award Scheme (2013/36/38-BRNS/2350, dt.25-11-2013) by Board of Research in Nuclear Sciences (BRNS), Dept. of Atomic Energy, Govt. of India. BF has a financial interest in CorticoMetrics, a company whose medical pursuits focus on brain imaging and measurement technologies. BF’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies.

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Authors and Affiliations

  1. Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Charlestown, Massachusetts, USA

    Subhranil Koley, Caterina Mainero, Bruce Fischl & Iman Aganj

  2. School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Subhranil Koley & Chandan Chakraborty

  3. Radiology Department, Harvard Medical School, Boston, Massachusetts, USA

    Caterina Mainero, Bruce Fischl & Iman Aganj

  4. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Bruce Fischl

Authors
  1. Subhranil Koley
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  2. Chandan Chakraborty
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  3. Caterina Mainero
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  4. Bruce Fischl
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  5. Iman Aganj
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Corresponding author

Correspondence to Iman Aganj .

Editor information

Editors and Affiliations

  1. University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

    Alessandro Crimi

  2. TU München, Computer Science, Munich, Germany

    Bjoern Menze

  3. Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  4. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  5. Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Stefan Winzeck

  6. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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© 2016 Springer International Publishing AG

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Koley, S., Chakraborty, C., Mainero, C., Fischl, B., Aganj, I. (2016). A Fast Approach to Automatic Detection of Brain Lesions. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-55524-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55523-2

  • Online ISBN: 978-3-319-55524-9

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