atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning

Peretzke, Robin; Maier-Hein, Klaus H.; Bohn, Jonas; Kirchhoff, Yannick; Roy, Saikat; Oberli-Palma, Sabrina; Becker, Daniela; Lenga, Pavlina; Neher, Peter

doi:10.1007/978-3-031-43993-3_23

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2024

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Cited by 2 publications

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Abstract: Semi-automatic White Matter Tract Segmentation using Active Learning atTRACTive

Peretzke,

Maier-Hein,

Bohn

et al. 2024

Informatik Aktuell

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Abstract: Semi-automatic White Matter Tract Segmentation using Active Learning atTRACTive

Peretzke,

Maier-Hein,

Bohn

et al. 2024

Informatik Aktuell

View full text Add to dashboard Cite

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

Joshi,

Li,

Parikh

et al. 2024

Front. Neurosci.

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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.

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atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning

Cited by 2 publications

References 20 publications

Abstract: Semi-automatic White Matter Tract Segmentation using Active Learning atTRACTive

Abstract: Semi-automatic White Matter Tract Segmentation using Active Learning atTRACTive

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

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