Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans

Salahuddin, Zohaib; Lenga, Matthias; Nickisch, Hannes

doi:10.1109/isbi48211.2021.9434002

Cited by 8 publications

(25 citation statements)

References 9 publications

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“…13 A similar strategy is adopted in deep learning methods. 14,15 Instead of computing the local vessel direction by handcrafted features, a convolutional neural network (CNN) is trained to predict it. Another propagation mechanism involves searching the candidate centerline point in the area surrounding the current location.…”

Section: Introductionmentioning

confidence: 99%

“…To further increase the degree of automation, some methods also perform branch detection when tracing the centerline. 7,13,15,16 Path-based models extract the entire centerline by computing the minimum cost path between two points usually provided by the user. The vesselness feature map generated by a hand-crafted image filter such as the Hessian matrix-based method 6,9,10,17 is used for the cost function, this being crucial for the accuracy of these methods.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, separate algorithms are used to finish these tasks. 15,16 In this work, no additional operations are needed. Instead, we solved those problems by directly observing the number of intersections between a sphere and the centerlines.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed COronary Artery Centerline Tracker (COACT) framework adopts a point-based strategy which gradually searches for the next centerline point based on the local image patch centered at the current location. Instead of predicting the local centerline direction, [13][14][15] this study directly models the intersections between a sample sphere and the centerlines of the coronary artery tree by the proposed collapse-expand graph neural network (CEGNN), which is a variant of the graph neural network (GNN). 18 The GNN is the neural network defined on the graph.…”

Section: Introductionmentioning

confidence: 99%

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COACT: Coronary artery centerline tracker

Li,

Ji,

Zhang

et al. 2023

Medical Physics

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BackgroundThe curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images.PurposeThe artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process.MethodsWe propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree.ResultsTwo coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter‐observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter‐observer agreement, 98.39%.ConclusionsWe have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice‐doc.netlify.app) for further studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COACT: Coronary artery centerline tracker

Li,

Ji,

Zhang

et al. 2023

Medical Physics

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“…and have since become the de facto standard for solving computer vision problems. Deep learning has also demonstrated state-of-the-art performance on many medical imaging challenges related to classification [15,3], segmentation [171,156,17] and other tasks [111].…”

Section: Introductionmentioning

confidence: 99%

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods

Salahuddin¹,

Woodruff²,

Chatterjee³

et al. 2021

Preprint

Self Cite

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Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision making process. Therefore, there is a need to ensure interpretability of deep neural networks before they can be incorporated in the routine clinical workflow. In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models for medical image analysis applications based on the type of generated explanations and technical similarities. Furthermore, we report the progress made towards evaluating the explanations produced by various interpretability methods. Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.

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An Overview Of Interpretability Techniques For Explainable Artificial Intelligence (XAI) In Deep Learning-Based Medical Image Analysis

Venkatesh

et al. 2023

2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)

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Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans

Cited by 8 publications

References 9 publications

COACT: Coronary artery centerline tracker

COACT: Coronary artery centerline tracker

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods

An Overview Of Interpretability Techniques For Explainable Artificial Intelligence (XAI) In Deep Learning-Based Medical Image Analysis

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