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

Andica, Christina; Kamagata, Koji; Aoki, Shigeki

doi:10.1007/s12565-023-00715-9

Cited by 6 publications

(3 citation statements)

References 95 publications

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“…However, even when these guidelines are followed, the quality of tractography results varies considerably 104 . Further research on automatic ROI placement methods and the automatic segmentation of major white matter tracts is required to solve this problem 105,106 …”

Section: Diffusion Mri Tractographymentioning

confidence: 99%

Advancements in Diffusion MRI Tractography for Neurosurgery

Kamagata,

Andica,

Uchida

et al. 2023

Invest Radiol

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Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.

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Section: Diffusion Mri Tractographymentioning

confidence: 99%

Advancements in Diffusion MRI Tractography for Neurosurgery

Kamagata,

Andica,

Uchida

et al. 2023

Invest Radiol

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“…Regarding the calculation formulas of the Cross-Level Residual Connections Module, as shown in the Appendix, there are formula (1), formula (2), formula (3), formula (4), formula (5), formula (6), formula (7). In these formulas, y represents the computation result of each group convolution block, α and γ represent the coefficients of the cross-level connections, K represents the convolution kernels of the blocks, b represents the normalization coefficients, Elu represents the activation function.…”

Section: Multi-layer Cross-connected Residual Mapping Modulementioning

confidence: 99%

“…The current clinical tools for detecting white matter abnormalities include observing MRI images ( 5 ), using neural networks to automatically segment and classify MRI images to determine the presence of abnormalities, analyzing the chemical composition of brain regions using magnetic resonance spectroscopy ( 6 , 7 ). The use of 3D medical images can enhance clinical visualization during patient treatment ( 8 ), deep learning segmentation algorithms have demonstrated superior performance in segmenting large collections of data.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of white matter hyperintensities and correlation analysis for cerebral small vessel disease

Zhang

Tian

et al. 2023

Front. Neurol.

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ObjectiveCerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.MethodsThis paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.ResultExperimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.ConclusionThe paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research.

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Fiber‐Specific White Matter Alterations in Parkinson's Disease Patients with GBA Gene Mutations

Andica,

Kamagata,

Uchida

et al. 2023

Movement Disorders

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BackgroundPatients with Parkinson's disease (PD) carrying GBA gene mutations (GBA‐PD) have a more aggressive disease course than those with idiopathic PD (iPD).ObjectiveThe objective of this study was to investigate fiber‐specific white matter (WM) differences in nonmedicated patients with early‐stage GBA‐PD and iPD using fixel‐based analysis, a novel technique to assess tract‐specific WM microstructural and macrostructural features comprehensively.MethodsFixel‐based metrics, including microstructural fiber density (FD), macrostructural fiber‐bundle cross section (FC), and a combination of FD and FC (FDC), were compared among 30 healthy control subjects, 16 patients with GBA‐PD, and 35 patients with iPD. Associations between FDC and clinical evaluations were also explored using multiple linear regression analyses.ResultsPatients with GBA‐PD showed significantly lower FD in the fornix and superior longitudinal fasciculus than healthy control subjects, and lower FC in the corticospinal tract (CST) and lower FDC in the CST, middle cerebellar peduncle, and striatal‐thalamo‐cortical pathways than patients with iPD. Contrarily, patients with iPD showed significantly higher FC and FDC in the CST and striatal‐thalamo‐cortical pathways than healthy control subjects. In addition, lower FDC in patients with GBA‐PD was associated with reduced glucocerebrosidase enzyme activity, lower cerebrospinal fluid total α‐synuclein levels, lower Montreal Cognitive Assessment scores, lower striatal binding ratio, and higher Unified Parkinson's Disease Rating Scale Part III scores.ConclusionsWe report reduced fiber‐specific WM density and bundle cross‐sectional size in patients with GBA‐PD, suggesting neurodegeneration linked to glucocerebrosidase deficiency, α‐synuclein accumulation, and poorer cognition and motor functions. Conversely, patients with iPD showed increased fiber bundle size, likely because of WM reorganization. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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

Cited by 6 publications

References 95 publications

Advancements in Diffusion MRI Tractography for Neurosurgery

Advancements in Diffusion MRI Tractography for Neurosurgery

Automatic segmentation of white matter hyperintensities and correlation analysis for cerebral small vessel disease

Fiber‐Specific White Matter Alterations in Parkinson's Disease Patients with GBA Gene Mutations

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