Cerebrovascular segmentation in phase-contrast magnetic resonance angiography by multi-feature fusion and vessel completion

Chen, Cheng; Zhou, Kangneng; Guo, Xiangming; Wang, Zhiliang; Xiao, Ruoxiu; Wang, Guangzhi

doi:10.1016/j.compmedimag.2022.102070

Cited by 11 publications

(2 citation statements)

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“…Magnetic Resonance Imaging (MRI) is a commonly used brain imaging method owing to its advantages of no radiation use, high resolution, and almost no allergic reaction. [68,69] MRI images for brain disease examination commonly contain T1weighted (T1), T1-weighted imaging with Contrast Enhancement (T1-CE), T2-weighted (T2), and FLuid Attenuated Inversion Recovery imaging (FLAIR) volumes. [70] Since brain disease diagnosis depends on the professional knowledge and clinical experience of doctors, efficient auxiliary diagnostic methods play a critical role in clinical practice.…”

Section: Brain Disease Diagnosismentioning

confidence: 99%

“…Therefore, brain imaging has become the most important tool to detect and diagnose brain diseases. Magnetic Resonance Imaging (MRI) is a commonly used brain imaging method owing to its advantages of no radiation use, high resolution, and almost no allergic reaction [68,69] . MRI images for brain disease examination commonly contain T1‐weighted (T1), T1‐weighted imaging with Contrast Enhancement (T1‐CE), T2‐weighted (T2), and FLuid Attenuated Inversion Recovery imaging (FLAIR) volumes [70] .…”

Section: Transformers In Brain Sciencesmentioning

confidence: 99%

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Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

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Section: Brain Disease Diagnosismentioning

confidence: 99%

Section: Transformers In Brain Sciencesmentioning

confidence: 99%