Automated recognition of the pericardium contour on processed CT images using genetic algorithms

Rodrigues, Érick Oliveira; Rodrigues, Lucas; Oliveira, Luiz Sérgio de; Conci, Aura; Liatsis, Panos

doi:10.1016/j.compbiomed.2017.05.013

Cited by 21 publications

(14 citation statements)

References 35 publications

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“…Researchers have developed machine learning algorithms to segment EAT. Rodrigues et al utilized a genetic algorithm to optimize the parameters of an ellipse that was used to simulate the pericardium contour (Rodrigues et al 2017b). After 10, 100, and 200 generations of genetic algorithm iteration, the percentage of epicardial fat engulfed by the ellipse was 97.3%, 98.8%, and 99.5%, respectively.…”

Section: Segmentation Of Eat and Pcatmentioning

confidence: 99%

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Zhang

Sun²,

Jiang

et al. 2021

European J Hybrid Imaging

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Background Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue. Methods We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation. Conclusion AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging.

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Section: Segmentation Of Eat and Pcatmentioning

confidence: 99%

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Zhang

Sun²,

Jiang

et al. 2021

European J Hybrid Imaging

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“…Jiang et al [10] proposed an evolutionary tabu search (TS) for optimising the parameters of an ellipse, with the goal of autonomously segmenting cells. In a previous work, we employed a similar idea to segment the pericardium layer of the human heart [11]. Parameter optimisation has also been used to reduce the computational complexity in image registration [12, 13].…”

Section: Literature Reviewmentioning

confidence: 99%

Fractal triangular search: a metaheuristic for image content search

Rodrigues

Liatsis

Satoru

et al. 2018

IET Image Processing

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“…Automated segmentation could potentially make ECF volume estimation more practical on a routine basis. Several approaches based on prior medical knowledge or non-deep learning techniques have been proposed for ECF segmentation, including genetic algorithms, region-of-interest selection with thresholding, and fuzzy c-means clustering ( Rodrigues et al, 2017 ; Militello et al, 2019 ; Zlokolica et al, 2017 ). Deep learning techniques have been applied to a wide variety of medical image segmentation problems with great success ( Singh et al, 2020 ; Kim et al, 2019 ; Hesamian et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Conventional non-deep learning methods have been proposed for ECF segmentation. Rodrigues et al (2017) proposed a genetic algorithm to recognize the pericardium contour on CT images. Militello et al (2019) proposed a semi-automatic approach using manual region-of-interest selection followed by thresholding segmentation.…”

Section: Introductionmentioning

confidence: 99%

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images

Siriapisith

Kusakunniran

Haddawy

2021

PeerJ Computer Science

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Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most difficult medical image segmentation tasks. Since the epicardial fat is infiltrated into the groove between cardiac chambers and is contiguous with cardiac muscle, segmentation requires location and voxel intensity. Recently, deep learning methods have been effectively used to solve medical image segmentation problems in several domains with state-of-the-art performance. This paper presents a novel approach to 3D segmentation of ECF by integrating attention gates and deep supervision into the 3D U-Net deep learning architecture. The proposed method shows significant improvement of the segmentation performance, when compared with standard 3D U-Net. The experiments show excellent performance on non-contrast CT datasets with average Dice scores of 90.06%. Transfer learning from a pre-trained model of a non-contrast CT to contrast-enhanced CT dataset was also performed. The segmentation accuracy on the contrast-enhanced CT dataset achieved a Dice score of 88.16%.

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Automated recognition of the pericardium contour on processed CT images using genetic algorithms

Cited by 21 publications

References 35 publications

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Fractal triangular search: a metaheuristic for image content search

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images

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