Evidence map›Paper›PMID 40751109›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

BEA-CACE: branch-endpoint-aware double-DQN for coronary artery centerline extraction in CT angiography images.

Yuyang Zhang, Gongning Luo, Wei Wang, Shaodong Cao, Suyu Dong, Daren Yu, Xiaoyun Wang, Kuanquan Wang

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yuyang ZhangFaculty of Computing, Harbin Institute of Technology, Harbin, 150001, China.
Gongning LuoFaculty of Computing, Harbin Institute of Technology, Harbin, 150001, China.
Wei WangSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China.
Shaodong CaoDepartment of Radiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Suyu DongCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
Daren YuDepartment of Cardiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Xiaoyun WangDepartment of Cardiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Kuanquan WangFaculty of Computing, Harbin Institute of Technology, Harbin, 150001, China. wangkq@hit.edu.cn.

Funding

National Natural Science Foundation of China 62001141National Natural Science Foundation of China 62001144National Natural Science Foundation of China 62272135National Natural Science Foundation of China 62372135Science, Technology and Innovation Commission of Shenzhen Municipality JCYJ20210324131800002Science, Technology and Innovation Commission of Shenzhen Municipality RCBS20210609103820029
6 · The paper itself

Abstract

purposeIn order to automate the centerline extraction of the coronary tree, three challenges must be addressed: tracking branches automatically, passing through plaques successfully, and detecting endpoints accurately. This study aims to develop a method to solve the three challenges.

methodsWe propose a branch-endpoint-aware coronary centerline extraction framework. The framework consists of a deep reinforcement learning-based tracker and a 3D dilated CNN-based detector. The tracker is designed to predict the actions of an agent with the objective of tracking the centerline. The detector identifies bifurcation points and endpoints, assisting the tracker in tracking branches and terminating the tracking process automatically. The detector can also estimate the radius values of the coronary artery.

resultsThe method achieves the state-of-the-art performance in both the centerline extraction and radius estimate. Furthermore, the method necessitates minimal user interaction to extract a coronary tree, a feature that surpasses other interactive methods.

conclusionThe method can track branches automatically, pass through plaques successfully and detect endpoints accurately. Compared with other interactive methods that require multiple seeds, our method only needs one seed to extract the entire coronary tree.

Indexed as

Computed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseCoronary VesselsRadiographic Image Interpretation, Computer-AssistedAlgorithmsDeep LearningHumansImaging, Three-DimensionalCoronary artery centerline extractionCoronary CT angiographyDeep learningDeep reinforcement learning

Identifiers

PMID40751109

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.