Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology

Moshe, Y.; Bashat, Dafna Ben; Hananis, Zeev; Teicher, Mina; Artzi, Moran

doi:10.1177/15330338221131387

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…TractSeg-based corticospinal tract segmentation was implemented in 28 patients with brain masses adjacent to or displacing the corticospinal tract, and the automated algorithm was able to segment the bilateral corticospinal tracts (CSTs) in all patients, whereas the manual fiber segmentation approach failed to reconstruct the CSTs in 2 patients (Richards et al 2021 ). In a large cohort ( N = 625) of patients with various brain pathologies, TractSeg showed superior consistency in CST segmentation compared with the manual approach (Moshe et al 2022 ). TractSeg was also able to perform fiber bundle segmentation in the presence of brain abnormalities, such as enlarged ventricles (Fig.…”

Section: Tractsegmentioning

confidence: 99%

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

2023

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White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.

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Section: Tractsegmentioning

confidence: 99%

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

2023

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“…TractSeg has also been qualitatively validated in a tumour dataset with mostly successful results, with more complete segmentations in cases with minimally displacing tumours (Richards et al, 2021 ). In Moshe et al ( 2022 ), the authors trained their own TractSeg model, on approximately 500 datasets, to segment the corticospinal tract (CST) in brain tumour patients. The results were more reproducible than for the compared manual method, and obtained an average dice similarity score of 0.64, almost 25% worse than the performance in healthy data reported in the original TractSeg study (for the same tract).…”

Section: Introductionmentioning

confidence: 99%

Fibre orientation atlas guided rapid segmentation of white matter tracts

Young,

Aquilina,

Seunarine

et al. 2024

Human Brain Mapping

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Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data‐driven approaches including deep‐learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas‐based direct tract mapping technique called ‘tractfinder’, utilising tract‐specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10–20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro‐anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel‐wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep‐learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

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“…The lack of analogous large open clinical datasets is preventing the training of the supervised learning model tailored for patients, where the white matter structures deviate from the normative model. Despite the occasional use of the models trained on healthy individuals for some small collection of patients [14,17,10,2,12], the translation of these models to clinical data remains an open challenge.…”

Section: Introductionmentioning

confidence: 99%

Supervised White Matter Bundle Segmentation in Glioma Patients With Transfer Learning

Riccardi

Ghezzi

Amorosino

et al. 2023

Preprint

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In clinical neuroscience, the segmentation of the main white matter bundles is a mandatory premise for pre-operative neurosurgical planning, neuro-rehabilitation prognosis and monitoring of neuro-related diseases. The automation of the task of bundle segmentation is achieving a good accuracy on healthy individuals taking advantage of data driven approaches and deep learning models. The lack of large clinical datasets is preventing the translation of these results to patients. Inference on patients data with model trained on healthy population is not effective because of domain shift. This study aims to carry out an empirical analysis to investigate how techniques of transfer learning might be beneficial to overcome these limitations. For our analysis, we consider an open publicly available dataset with hundreds of individuals and a clinical dataset with several tens of glioma patients. We focus our preliminary investigation on corticospinal tract. The results show that transfer learning might be effective to overcome the component of domain shift related to systemic bias. Nevertheless, the alterations induced by the tumors are not successfully managed by transfer learning and are still preventing the successful translation of model trained on healthy population.

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Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology

Cited by 5 publications

References 23 publications

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

Fibre orientation atlas guided rapid segmentation of white matter tracts

Supervised White Matter Bundle Segmentation in Glioma Patients With Transfer Learning

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