An Effective Enhancement and Segmentation of Coronary Arteries in 2D Angiograms

Zai, Sammer; Abbas, Asad

doi:10.1109/icscee.2018.8538411

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The necessity of configuring sensitivity parameters for the Frangi filter restricts its adaptability in generalization and complex structure detection, while U-Net segmentation methods exhibit proficiency in learning intricate features without such constraints. Furthermore, the incorporation of U-Net in the segmentation process ensures the accurate identification of bifurcation regions, a guarantee that is occasionally lacking in the application of the Frangi filter [13]. The process begins with the utilization of U-Net [12] to identify a specific region of interest within the image (i.e.…”

Section: Of 27mentioning

confidence: 99%

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Ochoa-Astorga,

WANG,

et al. 2024

Preprint

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Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

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Section: Of 27mentioning

confidence: 99%

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Ochoa-Astorga,

WANG,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Configuring sensitivity parameters for the Frangi filter limits its adaptability and complex structure detection, while U-Net segmentation methods proficiently learn intricate features without such constraints. Additionally, incorporating U-Net ensures accurate identification of bifurcation regions, a feature occasionally lacking in the Frangi filter application [13]. The process begins with U-Net identifying the blood vessels' region of interest, followed by thinning, pattern detection, and characterization of feature points using the Fast Retina Keypoint (FREAK) descriptor [14].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Ochoa-Astorga,

Wang,

et al. 2024

Electronics

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Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing Fundus Image Vessel Segmentation (FIVES) dataset for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

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“…For instance, studies have explored the application of deep learning for vessel segmentation and stenosis identification on single frame images in 2D. In recent publications, segmentation of the main branches in the coronary artery tree and quantifying the degree of stenosis have been attempted [13][14][15][16][17]. Automatic detection of a stenosis on RCA was investigated by Moon et al [18] using a combined model pipeline, in which a keyframe extraction model predicted the relevant frame followed by another model predicting stenosis.…”

Section: Introductionmentioning

confidence: 99%

“…Classification of LCA and RCA has been attempted by Avram et al [17], reporting a F1 score of 0.93 on contrast-containing single frames. As these previous studies only utilized 2D informa-tion [13][14][15][16][17][18], a relevant frame extraction phase is needed for deploying the model into the real world. For training these models, extensive frame-level annotations are needed.…”

Section: Introductionmentioning

confidence: 99%

Classification of Left and Right Coronary Arteries in Coronary Angiographies Using Deep Learning

et al. 2022

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Multi-frame X-ray images (videos) of the coronary arteries obtained using coronary angiography (CAG) provide detailed information about the anatomy and blood flow in the coronary arteries and play a pivotal role in diagnosing and treating ischemic heart disease. Deep learning has the potential to quickly and accurately quantify narrowings and blockages of the arteries from CAG videos. A CAG consists of videos acquired separately for the left coronary artery and the right coronary artery (LCA and RCA, respectively). The pathology for LCA and RCA is typically only reported for the entire CAG, and not for the individual videos. However, training of stenosis quantification models is difficult when the RCA and LCA information of the videos are unknown. Here, we present a deep learning-based approach for classifying LCA and RCA in CAG videos. Our approach enables linkage of videos with the reported pathological findings. We manually labeled 3545 and 520 videos (approximately seven videos per CAG) to enable training and testing of the models, respectively. We obtained F1 scores of 0.99 on the test set for LCA and RCA classification LCA and RCA classification on the test set. The classification performance was further investigated with extensive experiments across different model architectures (R(2+1)D, X3D, and MVIT), model input sizes, data augmentations, and the number of videos used for training. Our results showed that CAG videos could be accurately curated using deep learning, which is an essential preprocessing step for a downstream application in diagnostics of coronary artery disease.

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An Effective Enhancement and Segmentation of Coronary Arteries in 2D Angiograms

Cited by 4 publications

References 15 publications

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration

Classification of Left and Right Coronary Arteries in Coronary Angiographies Using Deep Learning

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