Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network

Priya, C; Sudha, S.

doi:10.1007/s10916-019-1227-3

Cited by 16 publications

(13 citation statements)

References 24 publications

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“…Thus, the epicardial fat within the pericardium could be segmented and further evaluated [11]. Besides this, several methods are proposed to implement the segmentation and quantify the cardiac fat automatically [12][13][14][15][16][17][18][19][20]. In [21], the authors applied an atlas-based method for initializing the contour of the pericardium, and then analyzed the epicardial fat of the inside region of the pericardium (IRP).…”

Section: Introductionmentioning

confidence: 99%

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

et al. 2020

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“…Other works use different types of optimization algorithms to segment the image. For instance, in Priya and Sudha [47], EAT and paracardial adipose tissue are segmented using region growing, and the regions are then merged with the help of fruitfly-based optimization. The authors report a Dice coefficient of 0.987 for EAT segmentation, slightly larger than Rodrigues et al [20] on the same dataset.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

“…Ref. [47] also does not clarify if the authors used a separate training and testing dataset to evaluate the classification performance, or how many slices and patients the whole process was evaluated on.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review

et al. 2022

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Epicardial and pericardial adipose tissues (EAT and PAT), which are located around the heart, have been linked to coronary atherosclerosis, cardiomyopathy, coronary artery disease, and other cardiovascular diseases. Additionally, the volume and thickness of EAT are good predictors of CVD risk levels. Manual quantification of these tissues is a tedious and error-prone process. This paper presents a comprehensive and critical overview of research on the epicardial and pericardial adipose tissue segmentation and quantification methods, evaluates their effectiveness in terms of segmentation time and accuracy, provides a critical comparison of the methods, and presents ongoing and future challenges in the field. Described methods are classified into pericardial adipose tissue segmentation, direct epicardial adipose tissue segmentation, and epicardial adipose tissue segmentation via pericardium delineation. A comprehensive categorization of the underlying methods is conducted with insights into their evolution from traditional image processing methods to recent deep learning-based methods. The paper also provides an overview of the research on the clinical significance of epicardial and pericardial adipose tissues as well as the terminology and definitions used in the medical literature.

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“…Finally, the segmented image was used as a source to distinguish epicardial or mediastinal adipose tissue based on the interpolation between pixels. This segmentation methodology based on the optimal neural network showed excellent results presenting with a Dice cross-correlation of 98.7 for epicardial fat, 98.2 for mediastinal fat and 98.5 for pericardium (42).…”

Section: Ai: Epicardial and Pericardial Adipose Tissuesmentioning

confidence: 97%

“…Rodrigues et al proposed another method of epicardial, and mediastinal adipose tissue segmentation based on regression algorithms (41). The authors analyzed images according to the quantity of red pixels (i.e., epicardial fat), green pixels (i.e., mediastinal fat), blue pixels (i.e., pericardium), gray pixels (i.e., areas of interest to be segmented), black pixels (i.e., areas not to be segmented), (42). Ground truth images of fat depots was provided as input to the neural network classifier, trained to distinguish a fat-infested image from a non-fat one image.…”

Section: Ai: Epicardial and Pericardial Adipose Tissuesmentioning

confidence: 99%

Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review

Greco¹,

Salgado²,

Hecke³

et al. 2022

Quant Imaging Med Surg

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The present review summarizes the available evidence on artificial intelligence (AI) algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on computed tomography (CT) images. Body composition imaging is a novel concept based on quantitative analysis of body tissues. Manual segmentation of medical images allows to obtain quantitative and qualitative data on several tissues including epicardial and pericardial fat. However, since manual segmentation requires a considerable amount of time, the analysis of adipose tissue compartments based on AI has been proposed as an automatic, reliable, accurate and fast tool. The literature research was performed on March 2021 using MEDLINE PubMed Central and "adipose tissue artificial intelligence", "adipose tissue deep learning" or "adipose tissue machine learning" as keywords for articles search. Relevant articles concerning epicardial adipose tissue, pericardial adipose tissue and AI were selected. The evaluation of adipose tissue compartments can provide additional information on the pathogenesis and prognosis of several diseases, including cardiovascular. AI can assist physicians to obtain important information, possibly improving the patient's quality of life and identifying patients at risk of developing variable disorders.

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Adaptive Fruitfly Based Modified Region Growing Algorithm for Cardiac Fat Segmentation Using Optimal Neural Network

Cited by 16 publications

References 24 publications

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review

Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review

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