3D carotid artery segmentation using shape-constrained active contours

Huang, Xianjue; Wang, Jun; Li, Zhiyong

doi:10.1016/j.compbiomed.2022.106530

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…In this paper, we leveraged voxel-wise segmentation indices instead of classification indices, i.e., DSC, MSD and HD, to evaluate the performance of labeling model, given the anatomical morphology and 3D properties of vessels. The proposed model achieved the overall DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm, demonstrating superior results compared with the conventional segmentation of carotid lumens and the whole cerebral vessels ( Hemmati et al, 2017 ; Chen et al, 2021 ; Guo et al, 2021 ; Huang, Wang, and Li, 2023 ). Besides, we found that labeling performance of MCA, ACoA and PCoA seemed to decline in line with prior researches ( Dunas et al, 2017 ; Hilbert et al, 2022 ; Hong et al, 2023 ).…”

Section: Discussionmentioning

confidence: 89%

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography

Chen,

You,

Zhang

et al. 2024

Front. Physiol.

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Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs).Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model’s potential in enhancing clinicians’ precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss’ Kappa) were calculated to assess the differences in clinical performance of clinicians.Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians’ mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss’ Kappa) increased by 0.07 (p = 0.029).Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.

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Section: Discussionmentioning

confidence: 89%

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography

Chen,

You,

Zhang

et al. 2024

Front. Physiol.

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“…Furthermore, DL methods lack the utilization of anatomical priors, posing challenges in maintaining region consistency and achieving continuous segmentation. 13 The substantial hyperparameter search space and protracted training time further complicate the manual design of neural networks. 14 The lattice Boltzmann (LB) method has found applications in various image processing tasks such as denoising, inpainting, registration and segmentation for its inherent parallelism and clear physical interpretation.…”

Section: Introductionmentioning

confidence: 99%

“…While these methods achieve peak segmentation accuracy on this specific dataset, concerns arise regarding their robustness and generalizability due to the limited sample size. Furthermore, DL methods lack the utilization of anatomical priors, posing challenges in maintaining region consistency and achieving continuous segmentation 13 . The substantial hyperparameter search space and protracted training time further complicate the manual design of neural networks 14 …”

Section: Introductionmentioning

confidence: 99%

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation

Liu,

Wang,

et al. 2024

Medical Physics

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BackgroundGiven the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance.ObjectiveOur aim is to propose and validate a HS segmentation model that is both training‐free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state‐of‐the‐art techniques. The segmented HS can serve as a biomarker for studying the progression of AD.MethodsWe utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwu datasets.ResultsThe median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter‐group differences revealed that, except for the right rostral region in the ADNI dataset, the p‐values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini‐mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA, respectively.ConclusionsOur framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas‐based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression of AD.

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Uncertainty-Based Quality Assurance of Carotid Artery Wall Segmentation in Black-Blood MRI

Thibeau-Sutre,

Alblas,

Buurman

et al. 2023

Lecture Notes in Computer Science

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3D carotid artery segmentation using shape-constrained active contours

Cited by 3 publications

References 31 publications

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation

Uncertainty-Based Quality Assurance of Carotid Artery Wall Segmentation in Black-Blood MRI

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