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

Nayereh Ghazi, Mohammad Hadi Aarabi, Hamid Soltanian-Zadeh
Neuroinformatics. 2023-06-17; 21(3): 517-548
DOI: 10.1007/s12021-023-09636-4

1. Neuroinformatics. 2023 Jul;21(3):517-548. doi: 10.1007/s12021-023-09636-4.
Epub 2023 Jun 17.

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

Ghazi N(1), Aarabi MH(2)(3), Soltanian-Zadeh H(4)(5).

Author information:
(1)Control and Intelligent Processing Center of Excellence (CIPCE), School of
Electrical and Computer Engineering, College of Engineering, University of
Tehran, Tehran, 14399, Iran.
(2)Department of Neuroscience, University of Padova, Padova, Italy.
(3)Padova Neuroscience Center (PNC), University of Padova, Padova, Italy.
(4)Control and Intelligent Processing Center of Excellence (CIPCE), School of
Electrical and Computer Engineering, College of Engineering, University of
Tehran, Tehran, 14399, Iran. .
(5)Medical Image Analysis Laboratory, Departments of Radiology and Research
Administration, Henry Ford Health System, Detroit, MI, 48202, USA.
.

Quantitative analysis of white matter fiber tracts from diffusion Magnetic
Resonance Imaging (dMRI) data is of great significance in health and disease.
For example, analysis of fiber tracts related to anatomically meaningful fiber
bundles is highly demanded in pre-surgical and treatment planning, and the
surgery outcome depends on accurate segmentation of the desired tracts.
Currently, this process is mainly done through time-consuming manual
identification performed by neuro-anatomical experts. However, there is a broad
interest in automating the pipeline such that it is fast, accurate, and easy to
apply in clinical settings and also eliminates the intra-reader variabilities.
Following the advancements in medical image analysis using deep learning
techniques, there has been a growing interest in using these techniques for the
task of tract identification as well. Recent reports on this application show
that deep learning-based tract identification approaches outperform existing
state-of-the-art methods. This paper presents a review of current tract
identification approaches based on deep neural networks. First, we review the
recent deep learning methods for tract identification. Next, we compare them
with respect to their performance, training process, and network properties.
Finally, we end with a critical discussion of open challenges and possible
directions for future works.