scholarly journals High interstudy repeatability of automatic deep learnt biventricular CMR measurements

2021 ◽  
Vol 22 (Supplement_2) ◽  
Author(s):  
S Alabed ◽  
K Karunasaagarar ◽  
F Alandejani ◽  
P Garg ◽  
J Uthoff ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
Intraclass Correlation ◽  
Therapy Response ◽  
Left Ventricular ◽  
Dice Similarity Coefficient ◽  
Learning Approaches ◽  
Fully Automatic ◽  
Automatic Contouring

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Wellcome Trust (UK), NIHR (UK) Introduction Cardiac magnetic resonance (CMR) measurements have significant diagnostic and prognostic value. Accurate and repeatable measurements are essential to assess disease severity, evaluate therapy response and monitor disease progression. Deep learning approaches have shown promise for automatic left ventricular (LV) segmentation on CMR, however fully automatic right ventricular (RV) segmentation remains challenging. We aimed to develop a biventricular automatic contouring model and evaluate the interstudy repeatability of the model in a prospectively recruited cohort. Methods A deep learning CMR contouring model was developed in a retrospective multi-vendor (Siemens and General Electric), multi-pathology cohort of patients, predominantly with heart failure, pulmonary hypertension and lung diseases (n = 400, ASPIRE registry). Biventricular segmentations were made on all CMR studies across cardiac phases. To test the accuracy of the automatic segmentation, 30 ASPIRE CMRs were segmented independently by two CMR experts. Each segmentation was compared to the automatic contouring with agreement assessed using the Dice similarity coefficient (DSC).  A prospective validation cohort of 46 subjects (10 healthy volunteers and 36 patients with pulmonary hypertension) were recruited to assess interstudy agreement of automatic and manual CMR assessments. Two CMR studies were performed during separate sessions on the same day. Interstudy repeatability was assessed using intraclass correlation coefficient (ICC) and Bland-Altman plots.  Results DSC showed high agreement (figure 1) comparing automatic and expert CMR readers, with minimal bias towards either CMR expert. The scan-scan repeatability CMR measurements were higher for all automatic RV measurements (ICC 0.89 to 0.98) compared to manual RV measurements (0.78 to 0.98). LV automatic and manual measurements were similarly repeatable (figure 2). Bland-Altman plots showed strong agreement with small mean differences between the scan-scan measurements (figure 2). Conclusion Fully automatic biventricular short-axis segmentations are comparable with expert manual segmentations, and have shown excellent interstudy repeatability.

scholarly journals Deep Learning-Based Segmentation and Quantification of Retinal Capillary Non-Perfusion on Ultra-Wide-Field Retinal Fluorescein Angiography

2020 ◽  
Vol 9 (8) ◽  
pp. 2537
Author(s):  
Joan M. Nunez do Rio ◽  
Piyali Sen ◽  
Rajna Rasheed ◽  
Akanksha Bagchi ◽  
Luke Nicholson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescein Angiography ◽  
Automatic Segmentation ◽  
Intraclass Correlation ◽  
Vascular Diseases ◽  
Dice Similarity Coefficient ◽  
Wide Field ◽  
Distribution Analysis ◽  
Topographical Distribution ◽  
Circular Grid

Reliable outcome measures are required for clinical trials investigating novel agents for preventing progression of capillary non-perfusion (CNP) in retinal vascular diseases. Currently, accurate quantification of topographical distribution of CNP on ultrawide field fluorescein angiography (UWF-FA) by retinal experts is subjective and lack standardisation. A U-net style network was trained to extract a dense segmentation of CNP from a newly created dataset of 75 UWF-FA images. A subset of 20 images was also segmented by a second expert grader for inter-grader reliability evaluation. Further, a circular grid centred on the FAZ was used to provide standardised CNP distribution analysis. The model for dense segmentation was five-fold cross-validated achieving area under the receiving operating characteristic of 0.82 (0.03) and area under precision-recall curve 0.73 (0.05). Inter-grader assessment on the 20 image subset achieves: precision 59.34 (10.92), recall 76.99 (12.5), and dice similarity coefficient (DSC) 65.51 (4.91), and the centred operating point of the automated model reached: precision 64.41 (13.66), recall 70.02 (16.2), and DSC 66.09 (13.32). Agreement of CNP grid assessment reached: Kappa 0.55 (0.03), perfused intraclass correlation (ICC) 0.89 (0.77, 0.93), non-perfused ICC 0.86 (0.73, 0.92), inter-grader agreement of CNP grid assessment values are Kappa 0.43 (0.03), perfused ICC 0.70 (0.48, 0.83), non-perfused ICC 0.71 (0.48, 0.83). Automated dense segmentation of CNP in UWF-FA images achieves performance levels comparable to inter-grader agreement values. A grid placed on the deep learning-based automatic segmentation of CNP generates a reliable and quantifiable method of measurement of CNP, to overcome the subjectivity of human graders.

scholarly journals Deep learning based fully automatic segmentation of the left ventricular endocardium and epicardium from cardiac cine MRI

2021 ◽  
Vol 11 (4) ◽  
pp. 1600-1612
Author(s):  
Yan Wang ◽  
Yue Zhang ◽  
Zhaoying Wen ◽  
Bing Tian ◽  
Evan Kao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Left Ventricular ◽  
Cine Mri ◽  
Cardiac Cine ◽  
Fully Automatic

scholarly journals Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

2021 ◽  
Vol 10 (15) ◽  
pp. 3347
Author(s):  
Fabien Lareyre ◽  
Cédric Adam ◽  
Marion Carrier ◽  
Juliette Raffort
Keyword(s):  
Deep Learning ◽  
Expert System ◽  
Vascular System ◽  
Automatic Segmentation ◽  
Hybrid Approach ◽  
Vascular Diseases ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Feature Based ◽  
Fully Automatic

Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.

scholarly journals Computed Tomography Angiography under Deep Learning in the Treatment of Atherosclerosis with Rapamycin

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Fuguang Ji ◽  
Shuai Zhou ◽  
Zhangshuan Bi
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Computed Tomography Angiography ◽  
Atherosclerotic Lesion ◽  
Region Of Interest ◽  
Left Ventricular ◽  
Dice Similarity Coefficient ◽  
Imaging Features ◽  
Lesion Area ◽  
Imaging Characteristics

The clinical characteristics and vascular computed tomography (CT) imaging characteristics of patients were explored so as to assist clinicians in diagnosing patients with atherosclerosis. 316 patients with atherosclerosis who were hospitalized for emergency treatment were treated with rapamycin (RAPA) in the hospital. A group of manually delineated left ventricular myocardia (LVM) on the patient’s coronary computed tomography angiography (CCTA) were selected as the region of interest for imaging features extracted. The CCTA images of 80% of patients were randomly selected for training, and those of 20% of patients were used for verification. The correlation matrix method was used to remove redundant image omics features under different correlation thresholds. In the validation set, CCTA diagnostic parameters were about 40 times higher than the manually segmented data. The average dice similarity coefficient was 91.6%. The proposed method also produced a very small centroid distance (mean 1.058 mm, standard deviation 1.245 mm) and volume difference (mean 1.640), with a segmentation time of about 1.45 ± 0.51 s, compared to about 744.8 ± 117.49 s for physician manual segmentation. Therefore, the deep learning model effectively segmented the atherosclerotic lesion area, measured and assisted the diagnosis of future atherosclerosis clinical cases, improved medical efficiency, and accurately identified the patient’s lesion area. It had great application potential in helping diagnosis and curative effect analysis of atherosclerosis.

scholarly journals A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning

2021 ◽  
Author(s):  
Wing Keung Cheung ◽  
Robert Bell ◽  
Arjun Nair ◽  
Leon Menezies ◽  
Riyaz Patel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Regions Of Interest ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Two Dimensional ◽  
Computed Tomography Images ◽  
Graphical Processing

AbstractA fully automatic two-dimensional Unet model is proposed to segment aorta and coronary arteries in computed tomography images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Furthermore, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed within hospital computer networks where graphical processing units are typically not available.

scholarly journals Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Chen Huang ◽  
Junru Tian ◽  
Chenglang Yuan ◽  
Ping Zeng ◽  
Xueping He ◽  
...  
Keyword(s):  
Deep Vein Thrombosis ◽  
Lower Extremity ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Dice Similarity Coefficient ◽  
Vein Thrombosis ◽  
Contrast Enhanced ◽  
Fully Automatic ◽  
Segmentation Task ◽  
Deep Vein

Objective. Deep vein thrombosis (DVT) is a disease caused by abnormal blood clots in deep veins. Accurate segmentation of DVT is important to facilitate the diagnosis and treatment. In the current study, we proposed a fully automatic method of DVT delineation based on deep learning (DL) and contrast enhanced magnetic resonance imaging (CE-MRI) images. Methods. 58 patients (25 males; 28~96 years old) with newly diagnosed lower extremity DVT were recruited. CE-MRI was acquired on a 1.5 T system. The ground truth (GT) of DVT lesions was manually contoured. A DL network with an encoder-decoder architecture was designed for DVT segmentation. 8-Fold cross-validation strategy was applied for training and testing. Dice similarity coefficient (DSC) was adopted to evaluate the network’s performance. Results. It took about 1.5s for our CNN model to perform the segmentation task in a slice of MRI image. The mean DSC of 58 patients was 0.74± 0.17 and the median DSC was 0.79. Compared with other DL models, our CNN model achieved better performance in DVT segmentation (0.74± 0.17 versus 0.66±0.15, 0.55±0.20, and 0.57±0.22). Conclusion. Our proposed DL method was effective and fast for fully automatic segmentation of lower extremity DVT.

scholarly journals Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

2020 ◽  
Vol 14 ◽  
Author(s):  
Chenyi Zeng ◽  
Lin Gu ◽  
Zhenzhong Liu ◽  
Shen Zhao
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Learning Approaches ◽  
Review Of The Literature ◽  
Lesion Segmentation ◽  
Segmentation Methods ◽  
Future Direction

In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.

scholarly journals Genetic Analysis of Right Heart Structure and Function in 40,000 People

2021 ◽  
Author(s):  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Victor Nauffal ◽  
Mahan Nekoui ◽  
Samuel N. Friedman ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Deep Learning ◽  
Right Ventricular ◽  
Congenital Heart ◽  
Left Ventricular ◽  
Structure And Function ◽  
Right Heart ◽  
Heart Chamber ◽  
The Right ◽  
And Function

The heart evolved hundreds of millions of years ago. During mammalian evolution, the cardiovascular system developed with complete separation between pulmonary and systemic circulations incorporated into a single pump with chambers dedicated to each circulation. A lower pressure right heart chamber supplies deoxygenated blood to the lungs, while a high pressure left heart chamber supplies oxygenated blood to the rest of the body. Due to the complexity of morphogenic cardiac looping and septation required to form these two chambers, congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. Additionally, some diseases predominantly affect structures of the right heart, including arrhythmogenic right ventricular cardiomyopathy (ARVC) and pulmonary hypertension. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, the right ventricle, and the pulmonary artery, and then used those models to measure right heart structures in over 40,000 individuals from the UK Biobank with magnetic resonance imaging. We found associations between these measurements and clinical disease including pulmonary hypertension and dilated cardiomyopathy. We then conducted genome-wide association studies, identifying 104 distinct loci associated with at least one right heart measurement. Several of these loci were found near genes previously linked with congenital heart disease, such as NKX2-5, TBX3, WNT9B, and GATA4. We also observed interesting commonalities and differences in association patterns at genetic loci linked with both right and left ventricular measurements. Finally, we found that a polygenic predictor of right ventricular end systolic volume was associated with incident dilated cardiomyopathy (HR 1.28 per standard deviation; P = 2.4E-10), and remained a significant predictor of disease even after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic and clinical determinants of right heart structure and function.

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