ArticleJournal of medical systems2019
Coronary Calcium Detection Based on Improved Deep Residual Network in Mimics.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease.BMC medical informatics and decision making · 2025Article
- Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients.Scientific reports · 2025Article
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- PDC: Pearl Detection with a Counter Based on Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on coronary artery calcium quantification.Annals of translational medicine · 2021Article
- Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease.The British journal of radiology · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection algorithm based on the Single Shot MultiBox Detector (SSD) in Mimics is proposed in this paper. The algorithm firstly uses the aggregate channel feature model to preprocess the image to obtain the suspected calcium area, which greatly reduces the time of single-frame image detection. The basic network VGG-16 is replaced by Resnet-50, which introduces the identity mapping to solve the problem of reducing the detection accuracy when the number of network layers are increased. Finally, the powerful and flexible two-parameter loss function is used to optimize the training deep network and improve the network model generalization ability. Qualitative and quantitative experiments show that the performance of the proposed detection algorithm exceeds the existing calcium detection algorithms, and the detection efficiency is improved while ensuring the accuracy of calcium detection.
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Registered trials
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