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

Benčević, Marin; Galić, Irena; Habijan, Marija; Pižurica, Aleksandra

doi:10.3390/app12105217

Cited by 6 publications

(4 citation statements)

References 63 publications

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“…Statistical information and prior knowledge assimilated in algorithm to make it adaptive. The algorithm also has some limitations: (1) the segmentation results depend on the selection of growing seeds and growing conditions, which requires human intervention, (2) it works poorly for images with a large overlap of grayscale ranges, and (3) the pattern of region-growing is also sensitive to noise 21 . Therefore, region-growing is rarely used alone, many existing studies have combined it with other methods in order to achieve satisfactory performance 22 , 23 .…”

Section: Related Workmentioning

confidence: 99%

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation

Chen,

Yao,

Liu

et al. 2024

Sci Rep

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Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnosis and radiotherapy planning. However, current deep learning-based methods for 3D multi-organ segmentation face challenges such as the need for labor-intensive manual pixel-level annotations and high hardware resource demands, especially regarding GPU resources. To address these issues, we propose a 3D proxy-bridged region-growing framework specifically designed for the segmentation of the liver and spleen. Specifically, a key slice is selected from each 3D volume according to the corresponding intensity histogram. Subsequently, a deep learning model is employed to pinpoint the semantic central patch on this key slice, to calculate the growing seed. To counteract the impact of noise, segmentation of the liver and spleen is conducted on superpixel images created through proxy-bridging strategy. The segmentation process is then extended to adjacent slices by applying the same methodology iteratively, culminating in the comprehensive segmentation results. Experimental results demonstrate that the proposed framework accomplishes segmentation of the liver and spleen with an average Dice Similarity Coefficient of approximately 0.93 and a Jaccard Similarity Coefficient of around 0.88. These outcomes substantiate the framework's capability to achieve performance on par with that of deep learning methods, albeit requiring less guidance information and lower GPU resources.

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Section: Related Workmentioning

confidence: 99%

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation

Chen,

Yao,

Liu

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…By obtaining detailed images of the heart, healthcare professionals can identify issues, assess disease progression, and formulate precise treatment plans. Cardiac imaging techniques, such as echocardiography, cardiac magnetic resonance imaging (CMRI), and cardiac computed tomography (CCT), are essential tools in cardiology [139,210]. Cardiac imaging also contributes to the development and evaluation of novel cardiovascular therapies.…”

Section: Cardiac Imagingmentioning

confidence: 99%

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

Galić,

Habijan,

Leventić

et al. 2023

Electronics

Self Cite

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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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“…These tasks can help improve the accuracy and efficiency of CBCT imaging, leading to better treatment outcomes and more efficient dental practice. The increasing use of deep learning algorithms for segmentations [2] in medical imaging is changing the workflows in digital dental clinics [3]. For example, recent advancements in deep learning applied in CBCT in clinical applications have allowed the identification of problems including root fractures or dental implant failure predictions [4,5].…”

Section: Introductionmentioning

confidence: 99%

AI-Assisted CBCT Data Management in Modern Dental Practice: Benefits, Limitations and Innovations

et al. 2023

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Within the next decade, artificial intelligence (AI) will fundamentally transform the workflow of modern dental practice. This paper reviews the innovations and new roles of dental assistants in CBCT data management with the support of AI. Its use in 3D data management brings new roles for dental assistants. Cone beam computed tomography (CBCT) technology is, together with intraoral 3D scans and 3D facial scans, commonly used 3D diagnostic in a modern digital dental practice. This paper provides an overview of the potential benefits of AI implementation for semiautomated segmentations in standard medical diagnostic workflows in dental practice. It discusses whether AI tools can enable healthcare professionals to increase their reliability, effectiveness, and usefulness, and addresses the potential limitations and errors that may occur. The paper concludes that current AI solutions can improve current digital workflows including CBCT data management. Automated CBCT segmentation is one of the current trends and innovations. It can assist professionals in obtaining an accurate 3D image in a reduced period of time, thus enhancing the efficiency of the whole process. The segmentation of CBCT serves as a helpful tool for treatment planning as well as communicating the problem to the patient in an understandable way. This paper highlights a high bias risk due to the inadequate sample size and incomplete reporting in many studies. It proposes enhancing dental workflow efficiency and accuracy through AI-supported cbct data management

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

Cited by 6 publications

References 63 publications

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation

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

AI-Assisted CBCT Data Management in Modern Dental Practice: Benefits, Limitations and Innovations

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