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A Unified Approach For Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography
Jaeik Jeon;Jiyeon Kim;Yeonggul Jang;Yeonyee E. Yoon;Dawun Jeong;Youngtaek Hong;Seung-Ah Lee;Hyuk-Jae Chang
2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Doppler echocardiography offers critical insights into cardiac function and phases by quantifying blood flow velocities and evaluating myocardial motion. However, previous methods for automating Doppler analysis, ranging from initial signal processing techniques to advanced deep learning approaches, have been constrained by their reliance on electrocardiogram (ECG) data and their inability to proc...Show More
Fully Convolutional Hybrid Fusion Network With Heterogeneous Representations for Identification of S1 and S2 From Phonocardiogram
Yeonggul Jang;Juyeong Jung;Youngtaek Hong;Jina Lee;Hyunseok Jeong;Hackjoon Shim;Hyuk-Jae Chang
Heart auscultation is a simple and inexpensive first-line diagnostic test for the early screening of heart abnormalities. A phonocardiogram (PCG) is a digital recording of an analog heart sound acquired using an electronic stethoscope. A computerized algorithm for PCG analysis can aid in detecting abnormal signal patterns and support the clinical use of auscultation. It is important to detect fund...Show More
Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction
Sekeun Kim;Zhenxiang Jiang;Byron A. Zambrano;Yeonggul Jang;Seungik Baek;Sunkook Yoo;Hyuk-Jae Chang
Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However...Show More
Automatic detection and classification of thoracic diseases using deep learning algorithms have many applications supporting radiologists’ diagnosis and prognosis. However, in the medical field, the class-imbalanced problem is extremely common due to the differences in prevalence among diseases, making it difficult to develop these applications. Many GAN-based methods have been proposed to solve t...Show More
Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data
Wufeng Xue;Jiahui Li;Zhiqiang Hu;Eric Kerfoot;James Clough;Ilkay Oksuz;Hao Xu;Vicente Grau;Fumin Guo;Matthew Ng;Xiang Li;Quanzheng Li;Lihong Liu;Jin Ma;Elias Grinias;Georgios Tziritas;Wenjun Yan;Angélica Atehortúa;Mireille Garreau;Yeonggul Jang;Alejandro Debus;Enzo Ferrante;Guanyu Yang;Tiancong Hua;Shuo Li
Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression ...Show More
Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data
Wufeng Xue;Jiahui Li;Zhiqiang Hu;Eric Kerfoot;James Clough;Ilkay Oksuz;Hao Xu;Vicente Grau;Fumin Guo;Matthew Ng;Xiang Li;Quanzheng Li;Lihong Liu;Jin Ma;Elias Grinias;Georgios Tziritas;Wenjun Yan;Angélica Atehortúa;Mireille Garreau;Yeonggul Jang;Alejandro Debus;Enzo Ferrante;Guanyu Yang;Tiancong Hua;Shuo Li
Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography
Sunghee Jung;Soochahn Lee;Byunghwan Jeon;Yeonggul Jang;Hyuk-Jae Chang
Motion artifacts may occur in coronary computed tomography angiography (CCTA) due to the heartbeat and impede the clinician’s diagnosis of coronary arterial diseases. Thus, motion artifact correction of the coronary artery is required to quantify the risk of disease more accurately. We present a novel method based on deep learning for motion artifact correction in CCTA. Because the image of the co...Show More
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the large...Show More
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin
Coronary luminal and wall mask prediction using convolutional neural network
Y. Hong;Y-M. Hong;Y. Jang;S. Kim;B. Jeon;S. Jung;S. Ha;D. Han;H. Shim;H. J. Chang
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
A significant amount of research has been done on the segmentation of coronary arteries. However, the resulting automated boundary delineation is still not suitable for clinical utilization. The convolutional neural network was driving advances in the medical image processing. We propose the brief convolutional network (BCN) that automatically produces the labeled mask with the luminal and wall bo...Show More
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