CoChat
Sign Up
Cited by 28Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesMedical Image Segmentation Techniques

Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation

Open access
Chen, Yuqian, Zhang, Chaoyi, Song, Yang, Makris, Nikos, Rathi, Yogesh, Cai, Weidong, Zhang, Fan, O’Donnell, Lauren J.·2021·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-87234-2_47
Open this paper in CoChat — free accountOpen PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/978-3-030-87234-2_47

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Deep learning techniques have been increasingly applied for anatomical tract identification, showing high potential for future work and improvements in computational speed.

    “Lastly, deep learning techniques have been increasingly applied for anatomical tract identification, showing high potential for future work and improvements in computational speed (Chen et al., 2021; Gupta et al., 2017a; 2018; 2017b; Lam et al., 2018; Reisert et al., 2018; Wasserthal et al., 2018; 2019; Xu et al., 2019; Zhang et al., 2020a).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Chen et al., 2021

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 29

  1. [1]1. Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophysical journal 66 (1), 259-267 (1994)
  2. [2]2. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Mag. Res. Med 44 (4), 625-632 (2000)
  3. [3]3. Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: ECCV. pp. 132-149 (2018)
  4. [4]4. Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR. vol. 1, pp. 539-546 (2005)
  5. [5]5. Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV. pp. 1422-1430 (2015)
  6. [6]6. Fischl, B.: Freesurfer. Neuroimage 62 (2), 774-781 (2012)
  7. [8]8. Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. arXiv:1803.07728 (2018) · arXiv
  8. [10]10. Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., et al.: Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. Neuroimage 61 (4), 1083-1099 (2012)
  9. [11]11. Guo, X., Liu, X., Zhu, E., Yin, J.: Deep clustering with convolutional autoencoders. In: ICNIP. pp. 373-382 (2017)
  10. [12]12. Gupta, V., Thomopoulos, S.I., et al.: FiberNET: An ensemble deep learning framework for clustering white matter fibers. In: MICCAI. pp. 548-555 (2017)
  11. [13]13. Huerta, I., V´ azquez, A., L´ opez-L´ opez, N., Houenou, J., Poupon, C., Mangin, J.F., Guevara, P., Hern´ andez, C.: Inter-subject clustering of brain fibers from wholebrain tractography. In: EMBC. pp. 1687-1691. IEEE (2020)
  12. [15]15. Kolesnikov, A., Zhai, X., Beyer, L.: Revisiting self-supervised visual representation learning. In: CVPR. pp. 1920-1929 (2019)
  13. [16]16. Legarreta, J.H., Petit, L., Rheault, F., Theaud, G., Lemaire, C., Descoteaux, M., Jodoin, P.M.: Tractography filtering using autoencoders. arXiv :2010.04007 (2020) · arXiv
  14. [17]17. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of machine learning research 9 (11) (2008)
  15. [18]18. Maier-Hein, K.H., Neher, P.F., et al.: The challenge of mapping the human connectome based on diffusion tractography. Nature communications 8 (1), 1-13 (2017)
  16. [19]19. Malcolm, J.G., Shenton, M.E., Rathi, Y.: Filtered multitensor tractography. IEEE transactions on medical imaging 29 (9), 1664-1675 (2010)
  17. [20]20. Norton, I., Essayed, W.I., Zhang, F., Pujol, S., Yarmarkovich, A., et al.: SlicerDMRI: open source diffusion MRI software for brain cancer research. Cancer research 77 (21), e101-e103 (2017)
  18. [21]21. O'Donnell, L., Westin, C.F.: White matter tract clustering and correspondence in populations. In: MICCAI. pp. 140-147 (2005)
  19. [24]24. O'Donnell, L.J., Wells, W.M., Golby, A.J., Westin, C.F.: Unbiased groupwise registration of white matter tractography. In: MICCAI. pp. 123-130. Springer (2012)
  20. [25]25. Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv:1807.03748 (2018) · arXiv
  21. [26]26. Paszke, A., Gross, S., Massa, F., Lerer, A., et al.: Pytorch: An imperative style, high-performance deep learning library. arXiv :1912.01703 (2019) · arXiv
  22. [28]28. Tian, F., Gao, B., Cui, Q., Chen, E., Liu, T.Y.: Learning deep representations for graph clustering. In: AAAI. vol. 28 (2014)
  23. [29]29. Tun¸ c, B., Ingalhalikar, M., Parker, D., Lecoeur, J., Singh, N., Wolf, R.L., et al.: Individualized map of white matter pathways: connectivity-based paradigm for neurosurgical planning. Neurosurgery 79 (4), 568-577 (2016)
  24. [33]33. Wu, Y., Hong, Y., Ahmad, S., Lin, W., et al.: Tract dictionary learning for fast and robust recognition of fiber bundles. In: MICCAI. pp. 251-259 (2020)
  25. [34]34. Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: ICML. pp. 478-487. PMLR (2016)
  26. [35]35. Xu, C., Sun, G., Liang, R., Xu, X.: Vector field streamline clustering framework for brain fiber tract segmentation. arXiv preprint arXiv:2011.01795 (2020) · arXiv
  27. [36]36. Xu, D., Tian, Y.: A comprehensive survey of clustering algorithms. Annals of Data Science 2 (2), 165-193 (2015)
  28. [37]37. Yang, J., Parikh, D., Batra, D.: Joint unsupervised learning of deep representations and image clusters. In: CVPR. pp. 5147-5156 (2016)
  29. [39]39. Yoo, S.W., Guevara, P., Jeong, Y., Yoo, K., Shin, J.S., Mangin, J.F., Seong, J.K.: An example-based multi-atlas approach to automatic labeling of white matter tracts. PloS one 10 (7), e0133337 (2015)

Cited by

Sorted by citations

Authors
Chen, Y., Zhang, C., Song, Y., Makris, N., Rathi, Y., Cai, W., Zhang, F., & O’Donnell, L. J.
Affiliations
  • The University of Sydney (AU)
  • UNSW Sydney (AU)
  • Harvard University (US)
Corresponding author
Zhang, Fan
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2021
Pages
497-507
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-87234-2_47
OpenAlex
W3201718073
Cited by
28
References
2
Citations by year
2026: 3 · 2025: 4 · 2024: 5 · 2023: 11 · 2022: 4 · 2021: 1
FWCI
6.60
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Institutes of Health
  • National Institute of Mental Health (R01MH119222, R01MH074794, R01MH125860)
  • National Institute of Biomedical Imaging and Bioengineering (P41EB015902)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
tractography, unsupervised deep learning, self-supervised learning, anatomical segmentation, outlier removal, Human Connectome Project, brain connectivity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review