Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network

Benčević, Marin; Habijan, Marija; Galić, Irena

doi:10.1109/elmar52657.2021.9550936

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Even simpler similar approaches exist where the final EAT segmentation is obtained by thresholding the pericardium region and not using a deep learning model, such as Benčević et al [44]. The input to the network has two channels, the original CT slice and another channel where each pixel has the same value of the slice depth of that slice.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…This is a publicly available dataset of CT scans of 20 patients. This dataset is later used in many papers from this review [27,[36][37][38][43][44][45][46][47]49]. While using a common dataset means a more objective measure of the algorithm's performance, this dataset still only contains 20 patients from the same geographic area.…”

Section: Data Quality Quantity and Diversitymentioning

confidence: 99%

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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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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Section: Data Quality Quantity and Diversitymentioning

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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“…Examples include the segmentation of brain tumors in MRI [152], lung nodules in chest CT scans [153], polyps [154], and vessel delineation [155]. Additionally, they find widespread use in cardiovascular image segmentation tasks, encompassing the isolation of specific structures like the aorta [156,157], heart chambers [158][159][160], epicardial tissue [161], left atrial appendage [162,163], and coronary arteries [164]. Precise segmentation is invaluable as it facilitates quantification, classification, and visualization of medical image data, ultimately supporting more informed clinical decision-making processes.…”

Section: Image Segmentationmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

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Artificial intelligence (AI) advancements, especially deep learning, have significantly improved medical image processing and analysis in various tasks such as disease detection, classification, and anatomical structure segmentation. This work overviews fundamental concepts, state-of-the-art models, and publicly available datasets in the field of medical imaging. First, we introduce the types of learning problems commonly employed in medical image processing and then proceed to present an overview of commonly used deep learning methods, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), with a focus on the image analysis task they are solving, including image classification, object detection/localization, segmentation, generation, and registration. Further, we highlight studies conducted in various application areas, encompassing neurology, brain imaging, retinal analysis, pulmonary imaging, digital pathology, breast imaging, cardiac imaging, bone analysis, abdominal imaging, and musculoskeletal imaging. The strengths and limitations of each method are carefully examined, and the paper identifies pertinent challenges that still require attention, such as the limited availability of annotated data, variability in medical images, and the interpretability issues. Finally, we discuss future research directions with a particular focus on developing explainable deep learning methods and integrating multi-modal data.

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Hyperparameter-optimized Cross Patch Attention (CPAM) UNET for accurate ischemia and hemorrhage segmentation in CT images

Kamanli

2023

SIViP

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Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network

Cited by 6 publications

References 23 publications

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

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

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Hyperparameter-optimized Cross Patch Attention (CPAM) UNET for accurate ischemia and hemorrhage segmentation in CT images

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