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Automated brain tractography segmentation using curvature points

Published: 18 December 2016 Publication History

Abstract

Classification of brain fiber tracts is an important problem in brain tractography analysis. We propose a supervised algorithm which learns features for anatomically meaningful fiber clusters, from labeled DTI white matter data. The classification is performed at two levels: a) Grey vs White matter (macro level) and b) White matter clusters (micro level). Our approach focuses on high curvature points in the fiber tracts, which embodies the unique characteristics of the respective classes. Any test fiber is classified into one of these learned classes by comparing proximity using the learned curvature-point model (for micro level) and with a neural network classifier (at macro level). The proposed algorithm has been validated with brain DTI data for three subjects containing about 2,50,000 fibers per subject, and is shown to yield high classification accuracy (> 93%) at both macro and micro levels.

References

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M. Catani, R. Howard, S. Pajevic, and D. Jones. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage, 17(1):77--94, 2002.
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M. Maddah, A. Mewes, S. Haker, W. Grimson, and S. Warfield. Automated atlas-based clustering of white matter fiber tracts from dtmri. In Medical image computing and computer-assisted intervention (MICCAI 2005), pages 188--195. Springer, 2005.
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V. Nikulin and G. McLachlan. Identifying fiber bundles with regularised k-means clustering applied to the grid-based data. In International Joint Conference on Neural Networks (IJCNN 2010), pages 1--8. IEEE, 2010.
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L. O'Donnell and C. Westin. Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Transactions on Medical Imaging, 26(11):1562--1575, 2007.
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S. Wakana, H. Jiang, L. Nagae-Poetscher, P. V. Zijl, and S. Mori. Fiber tract-based atlas of human white matter anatomy. Radiology, 230(1):77--87, 2004.
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Cited By

View all
  • (2022)Segmentation of Whole-Brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve PointsMedical Image Computing and Computer Assisted Intervention – MICCAI 202210.1007/978-3-031-16431-6_18(185-195)Online publication date: 15-Sep-2022
  • (2021)TractNet: A Deep Learning Approach on 3D Curves for Segmenting White Matter Fibre Bundles2021 21st International Conference on Advances in ICT for Emerging Regions (ICter)10.1109/ICter53630.2021.9774801(75-80)Online publication date: 2-Dec-2021
  • (2019)One-Class SVM for Human Brain Segmentation2019 22nd International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT48885.2019.9038448(1-7)Online publication date: Dec-2019
  • Show More Cited By

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cover image ACM Other conferences
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing
December 2016
743 pages
ISBN:9781450347532
DOI:10.1145/3009977
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • Google Inc.
  • QI: Qualcomm Inc.
  • Tata Consultancy Services
  • NVIDIA
  • MathWorks: The MathWorks, Inc.
  • Microsoft Research: Microsoft Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

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Author Tags

  1. DTI
  2. fiber
  3. grey matter
  4. segmentation
  5. tractography
  6. white matter

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ICVGIP '16
Sponsor:
  • QI
  • MathWorks
  • Microsoft Research

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Cited By

View all
  • (2022)Segmentation of Whole-Brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve PointsMedical Image Computing and Computer Assisted Intervention – MICCAI 202210.1007/978-3-031-16431-6_18(185-195)Online publication date: 15-Sep-2022
  • (2021)TractNet: A Deep Learning Approach on 3D Curves for Segmenting White Matter Fibre Bundles2021 21st International Conference on Advances in ICT for Emerging Regions (ICter)10.1109/ICter53630.2021.9774801(75-80)Online publication date: 2-Dec-2021
  • (2019)One-Class SVM for Human Brain Segmentation2019 22nd International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT48885.2019.9038448(1-7)Online publication date: Dec-2019
  • (2019)FS2Net: Fiber Structural Similarity Network (FS2Net) for Rotation Invariant Brain Tractography Segmentation Using Stacked LSTM Based Siamese NetworkComputer Analysis of Images and Patterns10.1007/978-3-030-29891-3_40(459-469)Online publication date: 22-Aug-2019

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