Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Mar 19:18:1376570.
doi: 10.3389/fnins.2024.1376570. eCollection 2024.

A systematic review of automated methods to perform white matter tract segmentation

Ankita Joshi  1   2 , Hailong Li  1   2   3 , Nehal A Parikh  2   4 , Lili He  1   2   3   5
Affiliations

A systematic review of automated methods to perform white matter tract segmentation

Ankita Joshi et al. Front Neurosci. .

Abstract

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

Keywords: diffusion magnetic resonance imaging (dMRI); segmentation; systematic review; tract segmentation; tractography; white matter tract.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Flow diagram for articles retrieved in this study.
Figure 2
Figure 2
The above chart shows the number of studies included in each category.
Figure 3
Figure 3
Illustration of the direct voxel-based segmentation pipeline using the segmentation of the corpus callosum as a representative example. Refer to Table 3 for more details regarding the direct voxel-based segmentation methods.
Figure 4
Figure 4
Illustration of the streamline-based clustering pipeline. Refer to Table 4 for more details regarding the clustering methods included in this review.
Figure 5
Figure 5
Illustration of the streamline-based classification pipeline. Refer to Table 5 for more details regarding the classification methods included in this review used to assign labels to streamlines.
Figure 6
Figure 6
Illustration of the atlas-based method pipeline. Refer to Table 6 for more details regarding the atlas-based methods included in this review.
See this image and copyright information in PMC

References

    1. Avants B. B., Tustison N., Song G. (2009). Advanced normalization tools (ANTS). Insight J. 2, 1–35.
    1. Balakrishnan G., Zhao A., Sabuncu M. R., Guttag J., Dalca A. V. (2019). VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38, 1788–1800. doi: 10.1109/TMI.2019.2897538, PMID: - DOI - PubMed
    1. Basser P. J., Mattiello J., LeBihan D. (1994a). Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B 103, 247–254. doi: 10.1006/jmrb.1994.1037, PMID: - DOI - PubMed
    1. Basser P. J., Mattiello J., LeBihan D. (1994b). MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267. doi: 10.1016/S0006-3495(94)80775-1, PMID: - DOI - PMC - PubMed
    1. Basser P. J., Pajevic S., Pierpaoli C., Duda J., Aldroubi A. (2000). In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632. doi: 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O, PMID: - DOI - PubMed

Publication types

LinkOut - more resources