The influence of regions of interest on tractography virtual dissection protocols: general principles to learn and to follow

Rheault, François; Schilling, Kurt G.; Obaid, Sami; Begnoche, John P.; Cutting, Laurie E.; Descoteaux, Maxime; Landman, Bennett A.; Petit, Laurent

doi:10.1007/s00429-022-02518-6

Cited by 5 publications

(3 citation statements)

References 46 publications

(46 reference statements)

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“…While manual segmentation of tracts or virtual dissection methods were not a focus of this survey, multiple approaches have been proposed in the last decade that conduct fiber selection and anatomical labeling using expert knowledge ( Rheault et al, 2020 , 2022a , b ; Ille et al, 2021 ). These methods focus on improving the design of white matter dissection protocols to build more generalizable and reproducible methods.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

Joshi,

Li,

Parikh

et al. 2024

Front. Neurosci.

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“…the size and position of regions-of-interest used to extract DTI metrics, how they were drawn, etc.) which is important because recent work has shown that subtle variability in the size and position of regions of interest have downstream effects on DTI metrics 10 . Some readers may decry our decision to meta-analyse statistically heterogeneous data, but this was done purposively because forest plots provide an important graphical representation of measurement variation in relation to experimental conditions (e.g., b-values and N D ) and they summarise a large amount of information in an easy-to-interpret format.…”

Section: Discussionmentioning

confidence: 99%

Meta-analysis of the normal diffusion tensor imaging values of the peripheral nerves in the upper limb

Wade

Poruslrani

et al. 2023

Sci Rep

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Peripheral neuropathy affects 1 in 10 adults over the age of 40 years. Given the absence of a reliable diagnostic test for peripheral neuropathy, there has been a surge of research into diffusion tensor imaging (DTI) because it characterises nerve microstructure and provides reproducible proxy measures of myelination, axon diameter, fibre density and organisation. Before researchers and clinicians can reliably use diffusion tensor imaging to assess the ‘health’ of the major nerves of the upper limb, we must understand the “normal” range of values and how they vary with experimental conditions. We searched PubMed, Embase, medRxiv and bioRxiv for studies which reported the findings of DTI of the upper limb in healthy adults. Four review authors independently triple extracted data. Using the meta suite of Stata 17, we estimated the normal fractional anisotropy (FA) and diffusivity (mean, MD; radial, RD; axial AD) values of the median, radial and ulnar nerve in the arm, elbow and forearm. Using meta-regression, we explored how DTI metrics varied with age and experimental conditions. We included 20 studies reporting data from 391 limbs, belonging to 346 adults (189 males and 154 females, ~ 1.2 M:1F) of mean age 34 years (median 31, range 20–80). In the arm, there was no difference in the FA (pooled mean 0.59 mm2/s [95% CI 0.57, 0.62]; I2 98%) or MD (pooled mean 1.13 × 10–3 mm2/s [95% CI 1.08, 1.18]; I2 99%) of the median, radial and ulnar nerves. Around the elbow, the ulnar nerve had a 12% lower FA than the median and radial nerves (95% CI − 0.25, 0.00) and significantly higher MD, RD and AD. In the forearm, the FA (pooled mean 0.55 [95% CI 0.59, 0.64]; I2 96%) and MD (pooled mean 1.03 × 10–3 mm2/s [95% CI 0.94, 1.12]; I2 99%) of the three nerves were similar. Multivariable meta regression showed that the b-value, TE, TR, spatial resolution and age of the subject were clinically important moderators of DTI parameters in peripheral nerves. We show that subject age, as well as the b-value, TE, TR and spatial resolution are important moderators of DTI metrics from healthy nerves in the adult upper limb. The normal ranges shown here may inform future clinical and research studies.

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“…In addition to the volumetric overlap and density metrics DSC and density correlation, we measured the volumetric bundle adjacency as defined in Schilling et al ( 2021 ). However, to avoid confusion with the streamline‐based bundle adjacency (Garyfallidis et al, 2012 ; Radwan et al, 2022 ; Rheault et al, 2022 ) metric previously defined in Garyfallidis et al ( 2012 ), and to give more intuitive meaning to the obtained values, we will refer to it as bundle distance BD. It is computed by taking the mean of minimum distances from every non‐overlapping voxel, in each segmentation, to the closest voxel in the other segmentation (Figure 3 ).…”

Section: Methodsmentioning

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 influence of regions of interest on tractography virtual dissection protocols: general principles to learn and to follow

Cited by 5 publications

References 46 publications

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

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

Meta-analysis of the normal diffusion tensor imaging values of the peripheral nerves in the upper limb

Fibre orientation atlas guided rapid segmentation of white matter tracts

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