Semi-automatic vessel detection for challenging cases of peripheral arterial disease

Mistelbauer, Gabriel; Morar, Anca; Schernthaner, Rüdiger; Strassl, Andreas; Fleischmann, Dominik; Moldoveanu, Florica; Gröller, M. Eduard

doi:10.1016/j.compbiomed.2021.104344

Cited by 8 publications

(2 citation statements)

References 61 publications

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“…Their model demonstrated both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area) [95]. Mistelbauer et al found that the application of semi-automatic lower limb vessel segmentation tools to clinical workflow enabled expert physicians to readily identify all clinically relevant lower extremity arteries with an average sensitivity of 92.9%, an average specificity, and an overall accuracy of 99.9% while saving 39% of the time [96]. In 2020, Hippe et al developed a fully automated deep-learning-based algorithm called Fully Automated and Robust Analysis Technique for Popliteal Artery Evaluation (FRAPPE) to segment and quantify the popliteal artery wall for the Osteoarthritis Initiative (https://nda.nih.gov/oai/).…”

Section: Ai Application In Mri Pad Assessmentmentioning

confidence: 99%

Multi-Modality Imaging of Atheromatous Plaques in Peripheral Arterial Disease: Integrating Molecular and Imaging Markers

Wang

Nai

Gan

et al. 2023

IJMS

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Peripheral artery disease (PAD) is a common and debilitating condition characterized by the narrowing of the limb arteries, primarily due to atherosclerosis. Non-invasive multi-modality imaging approaches using computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging have emerged as valuable tools for assessing PAD atheromatous plaques and vessel walls. This review provides an overview of these different imaging techniques, their advantages, limitations, and recent advancements. In addition, this review highlights the importance of molecular markers, including those related to inflammation, endothelial dysfunction, and oxidative stress, in PAD pathophysiology. The potential of integrating molecular and imaging markers for an improved understanding of PAD is also discussed. Despite the promise of this integrative approach, there remain several challenges, including technical limitations in imaging modalities and the need for novel molecular marker discovery and validation. Addressing these challenges and embracing future directions in the field will be essential for maximizing the potential of molecular and imaging markers for improving PAD patient outcomes.

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Section: Ai Application In Mri Pad Assessmentmentioning

confidence: 99%

Multi-Modality Imaging of Atheromatous Plaques in Peripheral Arterial Disease: Integrating Molecular and Imaging Markers

Wang

Nai

Gan

et al. 2023

IJMS

View full text Add to dashboard Cite

show abstract

“…Prior work on US vessel segmentation have utilized shape and motion models [4,5,6,7] typically requiring initialization with seed points in the first frame. † These authors contributed equally.…”

Section: Introductionmentioning

confidence: 99%

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

Jiang¹,

Chen²,

Bharat³

et al. 2021

Preprint

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Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases. This, however, remains a challenging task, particularly for extremely small vessels that are difficult to visualize. We propose to leverage the rich spatiotemporal context available in ultrasound to improve segmentation of small-scale lower-extremity arterial vasculature. We describe efficient deep learning methods that incorporate temporal, spatial, and feature-aware contextual embeddings at multiple resolution scales while jointly utilizing information from Bmode and Color Doppler signals. Evaluating on femoral and tibial artery scans performed on healthy subjects by an expert ultrasonographer, and comparing to consensus expert ground-truth annotations of inner lumen boundaries, we demonstrate real-time segmentation using the contextaware models and show that they significantly outperform comparable baseline approaches.

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Automatic Ultrasound Vessel Segmentation with Deep Spatiotemporal Context Learning

Jiang¹,

Chen²,

Bharat³

et al. 2021

Simplifying Medical Ultrasound

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Semi-automatic vessel detection for challenging cases of peripheral arterial disease

Cited by 8 publications

References 61 publications

Multi-Modality Imaging of Atheromatous Plaques in Peripheral Arterial Disease: Integrating Molecular and Imaging Markers

Multi-Modality Imaging of Atheromatous Plaques in Peripheral Arterial Disease: Integrating Molecular and Imaging Markers

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

Automatic Ultrasound Vessel Segmentation with Deep Spatiotemporal Context Learning

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