Hierarchical segmentation and tracking of coronary arteries in 2D X-ray Angiography sequences

M’hiri, Faten; Le, Thai Hoang; Duong, Luc; Desrosiers, Christian; Cherief, Mohamed

doi:10.1109/icip.2015.7351092

Cited by 7 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The previous work introduced a multiscale graph-based method that combines vesselness features with a temporal prior: the multiscale Temporal Vessel Walker (TVW). This study extends the work presented in [6] as follows:…”

Section: Introductionsupporting

confidence: 87%

“…This is illustrated by the presence of more white pixels in the proposed results than in the polyline result in Fig.8. comparison to using TVW pixelwise and to the TVW within the multiscale approach [6]. Using the TVW method pixelwise, while we have a better recall value of 82%, there is limited precision.…”

Section: Comparing the Proposed Methods To Polyline Trackingmentioning

confidence: 90%

“…To solve the spatio-temporal segmentation task, the proposed solution is based on the Temporal Vessel Walker method introduced in [6]. The proposed method adapts the random-walks formulation [16], [17] for vessel segmentation in 2D X-ray images.…”

Section: Temporal Vessel Walkermentioning

confidence: 99%

“…It uses the SP-TVW method within a multi-size superpixel pipeline, rather than applying the method at different scales of the image. This indicated to be more efficient than using a multiscale framework [6]. 3.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 illustrates the results of the proposed work, where a specific artery is segmented and tracked in a moving X-ray sequence. This work was previously undertaken in [6]. The previous work introduced a multiscale graph-based method that combines vesselness features with a temporal prior: the multiscale Temporal Vessel Walker (TVW).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

M’hiri

Duong

Desrosiers

et al. 2016

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

The segmentation and tracking of coronary arteries (CAs) are critical steps for the computation of biophysical measurements in pediatric interventional cardiology. In the literature, most methods are focused on either segmenting the vessel lumen or on tracking the vessel centerline. However, they do not simultaneously combine the segmentation and tracking of a specific CA. This paper introduces a novel algorithm for CA segmentation and tracking from 2D X-ray angiography sequences. The proposed algorithm is based on the Temporal Vessel Walker (TVW) segmentation method, which combines graph-based formulation and temporal priors. Moreover, superpixel groups are used by TVW as image primitives to ensure a better extraction of the CA. The proposed algorithm, TVW with superpixels (SP-TVW), returns an accurate result to segment and track the artery along the angiogram. Quantitative results over 12 sequences of young patients show the accuracy of the proposed framework. The results return a mean recall of 84% in the dataset. In addition, the proposed method returned a Dice index of 70% in segmenting and tracking right coronary arteries and circumflex arteries. The performance of the proposed method surpasses the existing polyline method in tracking the centerline of CA with a more precise localization of the centerline, resulting in a smaller distance error of 0.23mm compared to 0.94mm.

show abstract

Section: Introductionsupporting

confidence: 87%

Section: Comparing the Proposed Methods To Polyline Trackingmentioning

confidence: 90%

Section: Temporal Vessel Walkermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

M’hiri

Duong

Desrosiers

et al. 2016

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

Image thresholding method based on Tsallis entropy correlation

Wang,

Fan

2024

Multimed Tools Appl

View full text Add to dashboard Cite

Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging

Iqbal,

Khalid,

Ullah

2024

J Real-Time Image Proc

View full text Add to dashboard Cite

In certain healthcare settings, such as emergency or critical care units, where quick and accurate real-time analysis and decision-making are required, the healthcare system can leverage the power of artificial intelligence (AI) models to support decision-making and prevent complications. This paper investigates the optimization of healthcare AI models based on time complexity, hyper-parameter tuning, and XAI for a classification task. The paper highlights the significance of a lightweight convolutional neural network (CNN) for analysing and classifying Magnetic Resonance Imaging (MRI) in real-time and is compared with CNN-RandomForest (CNN-RF). The role of hyper-parameter is also examined in finding optimal configurations that enhance the model’s performance while efficiently utilizing the limited computational resources. Finally, the benefits of incorporating the XAI technique (e.g. GradCAM and Layer-wise Relevance Propagation) in providing transparency and interpretable explanations of AI model predictions, fostering trust, and error/bias detection are explored. Our inference time on a MacBook laptop for 323 test images of size 100x100 is only 2.6 sec, which is merely 8 milliseconds per image while providing comparable classification accuracy with the ensemble model of CNN-RF classifiers. Using the proposed model, clinicians/cardiologists can achieve accurate and reliable results while ensuring patients’ safety and answering questions imposed by the General Data Protection Regulation (GDPR). The proposed investigative study will advance the understanding and acceptance of AI systems in connected healthcare settings.

show abstract

Hierarchical segmentation and tracking of coronary arteries in 2D X-ray Angiography sequences

Cited by 7 publications

References 12 publications

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences

Image thresholding method based on Tsallis entropy correlation

Explaining decisions of a light-weight deep neural network for real-time coronary artery disease classification in magnetic resonance imaging

Contact Info

Product

Resources

About