Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective

Ghazi, Nayereh; Aarabi, Mohammad Hadi; Soltanian-Zadeh, Hamid

doi:10.1007/s12021-023-09636-4

“…In this review paper, we have provided a systematic review of automated methods for white matter tract segmentation with respect to the most widely used public datasets for this task, the various categories of automated methods developed, and the evaluation metrics used to study the performance of the method. Although there are studies that have reviewed automated methods for brain tractography ( Poulin et al, 2019 ; Zhang et al, 2022 ) and also deep learning methods for tract segmentation ( Ghazi et al, 2023 ), to the best of our knowledge, a systematic review that focuses on automated methods for tract segmentation has not been published yet. This review paper underscores the methodological advancements in building automated methods, as evidenced through the 59 articles included in this review.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, manual methods remain the gold standard for delineating WM tracts and serve as a critical benchmark for validating alternative approaches. The advent of better imaging techniques, improved image quality and higher resolutions ( Van Essen et al, 2012 ), along with the application of sophisticated post-processing techniques, has driven a significant surge in the development of automated methods for tract segmentation ( Yamada et al, 2009 ; Essayed et al, 2017 ; Ghazi et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

“…Authors summarize the various categories that tractography segmentation methods fall under ( Zhang et al, 2022 ) when reviewing quantitative tractography methods for studying the brain’s structural connectivity in health and disease. Recently, authors in Ghazi et al (2023) have reviewed literature focusing on deep learning approaches for tract segmentation. In this work, we extend the scope by conducting a systematic and comprehensive review of automated approaches for the segmentation of white matter tracts in the last decade.…”

Section: Introductionmentioning

confidence: 99%

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

Joshi,

Li,

Parikh

et al. 2024

Front. Neurosci.

1

0

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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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“…In supervised learning (e.g., k-nearest neighbours or multilayer perceptron), the non-linear relationship between input variables (features) and output targets (labels) is uncovered using training instances, which can be subsequently used for prediction on new instances (testing instances) 29 . Supervised learning algorithms have been widely used in areas of neuroscience and neural engineering research including brain imaging analysis [30][31][32] , neuroinformatics [33][34][35] , and behavioural analysis [36][37][38] .…”

Section: /33mentioning

confidence: 99%

NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

Li,

Frederick,

George

et al. 2023

Preprint

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Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

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“…Subsequently the trained algorithm may be used for prediction on new, untested stimulation parameters (testing instances) [29]. The machine learning approach has been widely used in areas of neuroscience and neural engineering research including brain imaging analysis [30][31][32], neuroinformatics [33][34][35], and behavioral analysis [36][37][38]. Herein, we extend the application of machine learning models to predict electrical stimulation-induced neural tissue damage.…”

Section: Introductionmentioning

confidence: 99%

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

Li,

Frederick,

George

et al. 2024

J. Neural Eng.

0

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Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

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Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective

Cited by 7 publications

References 106 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

NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

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