Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique

Chen, Hung-Hsun; Liu, Chih-Min; Chang, Shih Lin; Chang, P; Chen, Wei Shiang; Pan, Yo Ming; Fang, Ssu Ting; Zhan, Shan Quan; Chuang, Chieh Mao; Lin, Yenn Jiang; Kuo, Ling; Wu, Mei-Han; Chen, Chun Ku; Chang, Yaxuan; Shiu, Yang Che; Chen, Shih Ann; Lu, Henry Horng Shing

doi:10.1016/j.ijcard.2020.03.075

Cited by 31 publications

(30 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Importantly, these algorithms are not fully automated to compute quantifiable measures from the output of segmentation tasks. Algorithms that do, such as those given in Chen et al (21), used ad hoc approaches that do not include mechanisms to detect or handle cases where image segmentation exercise fails which will inevitably affect the results of the computed quantities or the results of further analysis that are based on such quantities. This can be observed, for example, in our estimation of LAV in this study where DL without QC mechanism failed to correctly compute 10 out 337 LAV values.…”

Section: Summary Of Findingsmentioning

confidence: 99%

Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment

Abdulkareem

Brahier

Zou

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

ObjectivesCardiac computed tomography (CCT) is a common pre-operative imaging modality to evaluate pulmonary vein anatomy and left atrial appendage thrombus in patients undergoing catheter ablation (CA) for atrial fibrillation (AF). These images also allow for full volumetric left atrium (LA) measurement for recurrence risk stratification, as larger LA volume (LAV) is associated with higher recurrence rates. Our objective is to apply deep learning (DL) techniques to fully automate the computation of LAV and assess the quality of the computed LAV values.MethodsUsing a dataset of 85,477 CCT images from 337 patients, we proposed a framework that consists of several processes that perform a combination of tasks including the selection of images with LA from all other images using a ResNet50 classification model, the segmentation of images with LA using a UNet image segmentation model, the assessment of the quality of the image segmentation task, the estimation of LAV, and quality control (QC) assessment.ResultsOverall, the proposed LAV estimation framework achieved accuracies of 98% (precision, recall, and F1 score metrics) in the image classification task, 88.5% (mean dice score) in the image segmentation task, 82% (mean dice score) in the segmentation quality prediction task, and R2 (the coefficient of determination) value of 0.968 in the volume estimation task. It correctly identified 9 out of 10 poor LAV estimations from a total of 337 patients as poor-quality estimates.ConclusionsWe proposed a generalizable framework that consists of DL models and computational methods for LAV estimation. The framework provides an efficient and robust strategy for QC assessment of the accuracy for DL-based image segmentation and volume estimation tasks, allowing high-throughput extraction of reproducible LAV measurements to be possible.

show abstract

Section: Summary Of Findingsmentioning

confidence: 99%

Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment

Abdulkareem

Brahier

Zou

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…In cardiovascular CT research and quantitative reporting [6,17,[29][30][31], image segmentation is the starting point and the most time-consuming step. Even with advanced cardiovascular CT applications, such as CT-derived fractional flow reserve (FFR) [5] and three-dimensional printing [7,32,33], accurate image segmentation is one of the most critical steps.…”

Section: Segmentation Of Anatomic Structuresmentioning

confidence: 99%

“…If a segmentation algorithm with an accuracy similar to that of an expert is developed, radiological reporting will change, and advanced applications, such as 3). The most popular and essential targets for image segmentation are the cardiac chambers [4,31,[34][35][36][37] (Fig. 2).…”

Section: Segmentation Of Anatomic Structuresmentioning

confidence: 99%

“…When developing an AI segmentation algorithm, preparing and labeling the dataset for development is a fundamental and critical step [1,3]. Most studies use the same imaging technique for labeling and prediction (e.g., left atrial labeling on CCTA for AI training [labeling] and automatic segmentation of the left atrium on CCTA [prediction]) [4,17,31,38]. Some researchers have developed an AI algorithm that divides the four cardiac chambers into nonenhanced cardiac CT images [35,36].…”

Section: Segmentation Of Anatomic Structuresmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Intelligence to Cardiovascular Computed Tomography

Yang

2021

Korean J Radiol

View full text Add to dashboard Cite

Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.

show abstract

“…We need a powerful system as a diagnostic tool for most of lung diseases in children. We had developed methods based on machine learning for medical image analysis 20 and deep learning for the other types of medical images 21,22 . In this study, we used the recent development of deep learning techniques for the task of medical investigation.…”

mentioning

confidence: 99%

Diagnosis of common pulmonary diseases in children by X-ray images and deep learning

Chen

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources.

show abstract

Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique

Cited by 31 publications

References 17 publications

Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment

Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment

Application of Artificial Intelligence to Cardiovascular Computed Tomography

Diagnosis of common pulmonary diseases in children by X-ray images and deep learning

Contact Info

Product

Resources

About