Medical decision making for 5D cardiac model: Template matching technique and simulation of the fifth dimension

Sakly, Houneida; Saïd, Mourad Ben; Radhouane, Syrine; Tagina, Moncef

doi:10.1016/j.cmpb.2020.105382

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Our purpose: Contour segmentation þ watershed segmentation 5D (3D þ Time þ Blood flow) [27,28,29] Short axis cine Fiesta and its study of flow in apnea have an impact on extracted measurements and their precision for decision making. for purpose it is to develop a fifth dimension of flow which consists in extracting new measurements for medical diagnosis.…”

Section: Approach For Cardiac Imaging Dimension Conditions Of Acquisimentioning

confidence: 99%

Evaluation of the active contour and topographic watershed segmentation “assessment of the systolic ejection fraction in the left ventricular for medical assistance in 5D short axis cine MRI”

Sakly

Saïd²,

Tagina

2020

Heliyon

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A comparative study has been depicted between the contour and topographic watershed segmentation approach for short-axis 5D cardiac sequences with MRI for medical decision. The fifth dimension has been defined as the excitation of pixels based on the gray scale around the myocardium without consideration of the morphological structure of the heart in 3D and fourth dimension (time). Three patients were performed the first is healthy, the second has a genetic disease, and the third had a heart failure syndrome for a dimension ROI ¼ 150mm, average age is 54 years old, and mean of weight ¼ 86 kg. A contouring and watershed segmentation algorithm for a sample of 63 Cine Fiesta MRI sequences for short-axis cuts with Matlab and its in-box toolbox complements was implemented. For a healthy patient 13.4% tolerance rate for the estimation of the stroke fraction, 6.4% for a patient with genetic disease, 8.7% error rate for a patient with heart failure symptom. The results show that the regurgitation fraction by the contour approach for a patient case with symptom of the presence of a genetic disease is 0.0335% for an aortic valve, 0.248% for a mitral valve, an error rate 0.16% for estimating this parameter for the aortic orifice with the watershed segmentation approach. In return, for a patient with suspected heart failure (stenosis or regurgitation) the regurgitation fraction is estimated by 0% for aortic valve, 1.49 e þ 03 % for a mitral valve, an error rate 11.76% compared to the watershed segmentation approach. The results are validated clinically. The Optimization of the topographic watershed approach with mutual information was simulated for the extraction of measurements (ejection fraction, regurgitation rate) within the left ventricle for three patient types (healthy, genetic pathology and heart failure). The results are considered interesting compared to the clinical assessment.

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Section: Approach For Cardiac Imaging Dimension Conditions Of Acquisimentioning

confidence: 99%

Evaluation of the active contour and topographic watershed segmentation “assessment of the systolic ejection fraction in the left ventricular for medical assistance in 5D short axis cine MRI”

Sakly

Saïd²,

Tagina

2020

Heliyon

Self Cite

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“…In summary, AS's far-reaching impact on both human health and global economies underscores the urgency of innovative solutions. In the ever-evolving landscape of healthcare and medical research, the collaboration between academic institutions and hospitals plays an important role in advancing our understanding of complex medical phenomena, improving patient care, and driving innovation in the field [8]. This paper represents the culmination of a collaborative effort with Hospital Santa Maria (HSM), aimed at contributing to the forefront of healthcare research.…”

Section: Introductionmentioning

confidence: 99%

AI-Based Aortic Stenosis Classification in MRI Scans

Elvas,

Águas,

Ferreira

et al. 2023

Electronics

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Aortic stenosis (AS) is a critical cardiovascular condition that necessitates precise diagnosis for effective patient care. Despite a limited dataset comprising only 202 images, our study employs transfer learning to investigate the efficacy of five convolutional neural network (CNN) models, coupled with advanced computer vision techniques, in accurately classifying AS. The VGG16 model stands out among the tested models, achieving 95% recall and F1-score. To fortify the model’s robustness and generalization, we implement various data augmentation techniques, including translation, rotation, flip, and brightness adjustment. These techniques aim to capture real-world image variations encountered in clinical settings. Validation, conducted using authentic data from Hospital Santa Maria, not only affirms the clinical applicability of our model but also highlights the potential to develop robust models with a limited number of images. The models undergo training after the images undergo a series of computer vision and data augmentation techniques, as detailed in this paper. These techniques augment the size of our dataset, contributing to improved model performance. In conclusion, our study illuminates the potential of AI-driven AS detection in MRI scans. The integration of transfer learning, CNN models, and data augmentation yields high accuracy rates, even with a small dataset, as validated in real clinical cases.

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“…In this paper the concept and the modeling of the 5D cardiac system was described (3D model + temporal dimension functional dimension of the flow) detailed in our research work mentioned in (Sakly et al, 2019) and (Sakly et al, 2020). This strategy consists in the reconstructing of a 3D geometry of the descending aorta and diffuse a 2D viscous laminar fluid to detect the zones of narrowing.…”

Section: Introductionmentioning

confidence: 99%

Medical decision making for cardiac MRI “5D Model of the Descending Aorta for the Detection of Severe Stenosis”

Sakly¹,

Saïd

Tagina³

2020

Preprint

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Purpose:Depict an accurate 5D (x, y,z,Time+flow dimension) model for medical decision medical. The time-dependent instantaneous pressure gradient across the aorta proposed advanced measures for the detection of severe stenosis (Flow rate, Reynolds Number, velocity . . . ) Methods:A 74 cardiac MRI scan was performed and 3057 images for a patient aged 10 years, with a record of congenital valve and valvular aortic stenosis in tight MRI and coarctation (operated and then dilated) in the context of shone syndrome.

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Medical decision making for 5D cardiac model: Template matching technique and simulation of the fifth dimension

Cited by 5 publications

References 17 publications

Evaluation of the active contour and topographic watershed segmentation “assessment of the systolic ejection fraction in the left ventricular for medical assistance in 5D short axis cine MRI”

Evaluation of the active contour and topographic watershed segmentation “assessment of the systolic ejection fraction in the left ventricular for medical assistance in 5D short axis cine MRI”

AI-Based Aortic Stenosis Classification in MRI Scans

Medical decision making for cardiac MRI “5D Model of the Descending Aorta for the Detection of Severe Stenosis”

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