Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs

Wu, Dan; Wang, Xin; Bai, Junjie; Xu, Xiaoyang; Ouyang, Bin; Li, Yuwei; Zhang, Heye; Qi, Song; Cao, Kunlin; Yin, Youbing

doi:10.1007/s11548-018-1884-6

Cited by 46 publications

(33 citation statements)

References 14 publications

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“…Previously published methods for automatic labeling of coronary artery segments in CCTA utilized either atlas-based approaches [7][8][9] or machine learning. 10,11 Atlas-based methods perform matching between an unseen tree and a labeled atlas tree. These methods reported good performance but they require careful tuning to deal with substantial anatomical variability.…”

Section: Introductionmentioning

confidence: 99%

“…These methods reported good performance but they require careful tuning to deal with substantial anatomical variability. Machine learning-based segment labeling methods have used handcrafted features describing each segment's geometry 10,11 and location. 11 Akinyemi et al 10 trained a Gaussian classifier to predict labels for each segment individually, agnostic to the information regarding adjacent segments.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning-based segment labeling methods have used handcrafted features describing each segment's geometry 10,11 and location. 11 Akinyemi et al 10 trained a Gaussian classifier to predict labels for each segment individually, agnostic to the information regarding adjacent segments. Hence, subsequent postprocessing exploiting segment connectivity was required.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, subsequent postprocessing exploiting segment connectivity was required. Wu et al 11 first leveraged deep learning by training an architecture based on long short-term memory (LSTM) to directly learn multi-segment representations from input trees. Here, we present a novel approach to coronary tree segment labeling that represents the coronary tree segments as nodes in a graph and directly performs machine learning on this graph.…”

Section: Introductionmentioning

confidence: 99%

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Graph attention networks for segment labeling in coronary artery trees

Hampe

Wolterink

Collet

et al. 2021

Medical Imaging 2021: Image Processing

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Accurately labeled segments of the coronary artery trees are important for diagnostic reporting of coronary artery disease. As current automatic reporting tools do not consider anatomical segment labels, accurate automatic solutions for deriving these labels would be of great value. We propose an automatic method for labeling segments in coronary artery trees represented by centerlines automatically extracted from CCTA images. Using the connectivity between the centerlines, we construct a tree graph. Coronary artery segments are defined as edges of this graph and characterized by location and geometry features. The constructed coronary artery tree is transformed into a linegraph and used as input to a graph attention network, which is trained to classify labels of coronary artery segments. The method was evaluated on 71 CCTA images, achieving an F1-score of 92.4% averaged over all patients and segments. The results indicate that graph attention networks are suitable for coronary artery tree labeling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Graph attention networks for segment labeling in coronary artery trees

Hampe

Wolterink

Collet

et al. 2021

Medical Imaging 2021: Image Processing

View full text Add to dashboard Cite

show abstract

“…Wu, Dan, et al proposed a method based on long short-term memory (LSTM). The method establishes a TreeLab-Net combining a multilayer perceptron (MLP) encoder network and a bidirectional tree-structural long short-term memory [18]. The net uses the spatial locations and directions of arteries as features and performs an evaluation by a tenfold cross-validation.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Coronary Arteries in Coronary Computed Tomographic Angiography

Zhang

Xia

Zheng³

et al. 2020

IEEE Access

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Cardiovascular disease has seriously affected the lives of modern people. One of the most commonly used imaging methods for diagnosing cardiovascular disease is computed tomography angiography (CTA). To generate a diagnosis report for doctors, every coronary artery needs to be identified and segmented, including the right coronary artery (RCA), the posterior descending artery (PDA), the posterior lateral branch (PLB), the left circumflex (LCx), the left anterior descending branch (LAD), the ramus intermedius (RI), the obtuse marginal branches (OM1, OM2), and the diagonal branches (D1, D2). In this paper, we proposed a coronary artery automatic identification algorithm, which performs better in terms of accuracy than other similar algorithms and works efficiently. Normally, each Coronary Computed Tomographic Angiography (CCTA) dataset can be completed within seconds. This algorithm fully complies with the coronary label standard established by the Society of Cardiovascular Computed Tomography (SCCT). This algorithm has been put into operation in more than 100 hospitals for over one year. According to all previous tests, the labels obtained from the algorithm were compared with results manually corrected by several experts. Among 892 CCTA datasets, 95.96% of the labels obtained from the algorithms were correct. INDEX TERMS Automatic identification, computed tomography angiography, coronary artery.

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