Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Autor: Sekeun Kim, Zhenxiang Jiang, Byron A. Zambrano, Yeonggul Jang, Seungik Baek, Sunkook Yoo, Hyuk-Jae Chang

Publikováno v: IEEE Transactions on Medical Imaging. 42:196-208

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfdec336898d956e407e1dcc87f6990f
https://doi.org/10.1109/tmi.2022.3206142

Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms

Autor: Hyuk-Jae Chang, Sang-Eun Lee, Reza Arsanjani, Jaeik Jeon, Hyung-Bok Park, Ran Heo, Inki Moon, Sekeun Kim, Sun Kook Yoo

Publikováno v: The international journal of cardiovascular imaging.

Objectives: We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b097fa30cd206760a4055fbd6af1b816
https://pubmed.ncbi.nlm.nih.gov/35152371

Artificial Intelligence and Echocardiography

Autor: Sekeun Kim, Yeonyee E. Yoon, Hyuk Jae Chang

Publikováno v: Journal of Cardiovascular Imaging

Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnet

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ab637b52f84f57bb29d156974f7b1c2
https://pubmed.ncbi.nlm.nih.gov/34080347

Full Quantification of Left Ventricle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D + t Cine MRI

Autor: Hackjoon Shim, Hyuk Jae Chang, Yeonggul Jang, Sekeun Kim

Publikováno v: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges ISBN: 9783030120283
STACOM@MICCAI

Accurate quantification of left ventricle (LV) from cardiac image are valuable to evaluate ventricular function information such as stroke volume and ejection fraction. In this paper, we proposed a novel FCN architecture, which is trained in end-to-e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::af4d0d64a8c2a734891a1896fde21663
https://doi.org/10.1007/978-3-030-12029-0_51

Fully Automatic Segmentation of Coronary Arteries Based on Deep Neural Network in Intravascular Ultrasound Images

Autor: Hackjoon Shim, Byunghwan Jeon, Youngtaek Hong, Hyuk Jae Chang, Yeonggul Jang, Sekeun Kim

Publikováno v: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis ISBN: 9783030013639
CVII-STENT/LABELS@MICCAI

Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arterie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8c89e64fc2ebffd5529285b87fdc8a34
https://doi.org/10.1007/978-3-030-01364-6_18