An automated segmentation of coronary artery calcification using deep learning in specific region limitation

Zair, Asmae Mama; Cherfa, Assia; Cherfa, Yazid; Belkhamsa, Noureddine

doi:10.1007/s11517-023-02797-z

Cited by 2 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning, especially convolutional neural networks, has garnered considerable attention in recent years, particularly regarding image analysis [18][19][20][21][22]. The U-shaped fully convolutional network (U-Net) architecture [23] and its variants have excelled in segmentation tasks [24][25][26][27]. Several studies have demonstrated that deep learning techniques outperform machine learning techniques in automatically scoring aortic calcification [12,28].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Wang,

et al. 2023

Bioengineering

View full text Add to dashboard Cite

To investigate the performance of deep-learning-based algorithms for the automatic segmentation and quantification of abdominal aortic calcification (AAC) in lateral lumbar radiographs, we retrospectively collected 1359 consecutive lateral lumbar radiographs. The data were randomly divided into model development and hold-out test datasets. The model development dataset was used to develop U-shaped fully convolutional network (U-Net) models to segment the landmarks of vertebrae T12–L5, the aorta, and anterior and posterior aortic calcifications. The AAC lengths were calculated, resulting in an automatic Kauppila score output. The vertebral levels, AAC scores, and AAC severity were obtained from clinical reports and analyzed by an experienced expert (reference standard) and the model. Compared with the reference standard, the U-Net model demonstrated a good performance in predicting the total AAC score in the hold-out test dataset, with a correlation coefficient of 0.97 (p <0.001). The overall accuracy for the AAC severity was 0.77 for the model and 0.74 for the clinical report. Additionally, the Kendall coefficient of concordance of the total AAC score prediction was 0.89 between the model-predicted score and the reference standard, and 0.88 between the structured clinical report and the reference standard. In conclusion, the U-Net-based deep learning approach demonstrated a relatively high model performance in automatically segmenting and quantifying ACC.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Wang,

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

Integrated deep learning approach for automatic coronary artery segmentation and classification on computed tomographic coronary angiography

Muthusamy,

Murugesh

2024

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

An automated segmentation of coronary artery calcification using deep learning in specific region limitation

Cited by 2 publications

References 34 publications

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Integrated deep learning approach for automatic coronary artery segmentation and classification on computed tomographic coronary angiography

Contact Info

Product

Resources

About