Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images

Mousa, Doaa; Zayed, Nourhan; Yassine, Inas A.

doi:10.24297/ijct.v15i11.4376

“…No prior study has reported fully automated inline perfusion analysis using deep learning, but efforts had been made to segment perfusion MRI using different algorithms [46][47][48][49][50][51][52][53][54]. An early work [53] was semi-automated by drawing the ROI around heart and applying the GVF snake [55] to segment myocardial border.…”

Section: Discussionmentioning

confidence: 99%

“…An early work [53] was semi-automated by drawing the ROI around heart and applying the GVF snake [55] to segment myocardial border. Other published methods used active contours [51], active shape model [52] and level set based segmentation [46,50,54]. Many works require manual initialization [47][48][49][51][52][53][54] and rely on edge detection from high contrast perfusion phases when contrast agent arrives at LV.…”

Section: Discussionmentioning

confidence: 99%

“…Other published methods used active contours [51], active shape model [52] and level set based segmentation [46,50,54]. Many works require manual initialization [47][48][49][51][52][53][54] and rely on edge detection from high contrast perfusion phases when contrast agent arrives at LV. We proposed to utilize the temporal information through the whole bolus passage and applied deep neural network to perfusion CMR.…”

Section: Discussionmentioning

confidence: 99%

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Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning

Xue¹,

Davies²,

Brown³

et al. 2020

Radiology: Artificial Intelligence

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To develop a deep neural network-based computational workflow for inline myocardial perfusion analysis that automatically delineates the myocardium, which improves the clinical workflow and offers a "one-click" solution. Materials and Methods: In this retrospective study, consecutive adenosine stress and rest perfusion scans were acquired from three hospitals between October 1, 2018 and February 27, 2019. The training and validation set included 1825 perfusion series from 1034 patients (mean age, 60.6 years 6 14.2 [standard deviation]). The independent test set included 200 scans from 105 patients (mean age, 59.1 years 6 12.5). A convolutional neural network (CNN) model was trained to segment the left ventricular cavity, myocardium, and right ventricle by processing an incoming time series of perfusion images. Model outputs were compared with manual ground truth for accuracy of segmentation and flow measures derived on a global and per-sector basis with t test performed for statistical significance. The trained models were integrated onto MR scanners for effective inference. Results: The mean Dice ratio of automatic and manual segmentation was 0.93 6 0.04. The CNN performed similarly to manual segmentation and flow measures for mean stress myocardial blood flow (MBF; 2.25 mL/min/g 6 0.59 vs 2.24 mL/min/g 6 0.59, P = .94) and mean rest MBF (1.08 mL/min/g 6 0.23 vs 1.07 mL/min/g 6 0.23, P = .83). The per-sector MBF values showed no difference between the CNN and manual assessment (P = .92). A central processing unit-based model inference on the MR scanner took less than 1 second for a typical perfusion scan of three slices. Conclusion: The described CNN was capable of cardiac perfusion mapping and integrated an automated inline implementation on the MR scanner, enabling one-click analysis and reporting in a manner comparable to manual assessment.

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Deep Learning and Medical Imaging

Zayed

¹

,

Elnemr

²

2019

Advances in Healthcare Information Systems and Administration

1

0

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Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

show abstract

Deep Learning and Medical Imaging

Zayed

¹

,

Elnemr

²

2022

Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention

0

View full text Add to dashboard Cite

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

show abstract

Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images

Cited by 4 publications

References 9 publications

Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning

Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning

Deep Learning and Medical Imaging

Deep Learning and Medical Imaging

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