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Publication

Journals

2023

  1. Park, Hyung‐Bok, et al. "Risk factors based vessel‐specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry." Clinical cardiology 46.3 (2023): 320-327.
     

  2. Lee, Jina, et al. "Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising." Computers in Biology and Medicine 159 (2023): 106931.
     

  3. Lee, Seul Bi, et al. "Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation." Korean Journal of Radiology 24.4 (2023): 294.
     

  4. Han, Kyunghoon, et al. "Reconstruction of Partially Broken Vascular Structures in X-ray Images via Vesselness-loss-based Multi-scale Generative Adversarial Networks." IEEE Access (2023).
     

  5. Jeong, Hyunseok, et al. "Identifying Coronary Artery Calcification Using Chest X-ray Radiographs and Machine Learning: The Role of the Radiomics Score" Journal of thoracic imaging (2023);

2022

  1. Kim, S., Park, H. B., Jeon, J., Arsanjani, R., Heo, R., Lee, S. E., ... & Chang, H. J. "Fully automated quantification of cardiac chamber and function assessment in 2-D Echocardiography: clinical feasibility of deep learning-based algorithms.", The International Journal of Cardiovascular Imaging, Feb. 2022
     

  2. Han K, Jeon J, Jang Y, Jung S, Kim S, Shim H, Jeon B, Chang H, "Reconnection of Fragmented Parts of Coronary Arteries using Local Geometric Features in X-ray Angiography", Computers in Biology and Medicine, Feb. 2022
     

  3. Kim, S., Jiang, Z., Zambrano, B. A., Jang, Y., Baek, S., Yoo, S. K., & Chang, H. J. (2022). Deep learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction. IEEE Transactions on Medical Imaging.
     

  4. Lee, Seul Bi, et al. "Deep learning-based image conversion improves the reproducibility of computed tomography radiomics features: a phantom study." Investigative Radiology 57.5 (2022): 308-317.

2021

  1. Ha S, Jung S, Park H, Shin S, Arsanjani R, Hong Y, Lee B, Jang Y, Jeon B, Park S, Shim H, Chang H*, Assessment of Image Quality for Selective Intracoronary Contrast-injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in Swine, Yonsei Medical Journal, Accepted.
     

  2. Jeon B, Jung S, Shim H, Chang H*, “Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images,” Entropy, Jan. 2021.
     

  3. Ann K, Jang Y, Shim H, Chang H, "Multi-Scale Conditional Generative Adversarial Network for Small Diseases using Class Activation Region Influence Maximization", IEEE Access, Sep. 2021.

2020

  1. Jung, S., Lee, S., Jeon, B., Jang, Y., & Chang, H. J. (2020). Deep Learning Cross-Phase Style Transfer for Motion Artifact Correction in Coronary Computed Tomography Angiography. IEEE Access.
     

  2. Park, H. B., Jang, Y., Arsanjani, R., Nguyen, M. T., Lee, S. E., Jeon, B., ... & Lee, S. W. (2020). Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System. Yonsei Medical Journal, 61(2), 137-144.
     

  3. Cho, I. J., Sung, J. M., Kim, H. C., Lee, S. E., Chae, M. H., Kavousi, M., ... & Chang, H. J. (2020). Development and external validation of a deep learning algorithm for prognostication of cardiovascular outcomes. Korean circulation journal, 50(1), 72-84.

2019

  1. Park, K. M., Sung, J. M., Kim, W. J., An, S. K., Namkoong, K., Lee, E., & Chang, H. J. (2019). Population-based dementia prediction model using Korean public health examination data: A cohort study. PloS one, 14(2).
     

  2. Hong, Y., Park, H. B., Lee, B. K., Ha, S., Jang, Y., Jeon, B., ... & Chang, H. J. (2019). Clinical feasibility of catheter-directed selective intracoronary computed tomography angiography using an extremely low dose of iodine in patients with coronary artery disease. European radiology, 29(5), 2218-2225.
     

  3. Jeon, B., Jang, Y., Shim, H., & Chang, H. J. (2019). Identification of coronary arteries in CT images by Bayesian analysis of geometric relations among anatomical landmarks. Pattern Recognition, 96, 106958.
     

  4. Jia D, Jeon B (co-first author), Park H, Chang H, & Zhang LT. Image-Based Flow Simulations of Pre-and Post-left Atrial Appendage Closure in the Left Atrium. Cardiovascular Engineering and Technology 2019; 10(2):1-17.
     

  5. Kyunghoon Han, Jeon B. et al. “Robust coronary artery segmentation in 2D X-ray images using local patch-based re-connection methods”, The Korean Institute of Broadcast and Media Engineers. Special Issue (24-4).

2018

  1. Jung S, Lee S, Jeon B, Jang Y, & Chang H. Deep Learning Based Coronary Artery Motion Artifact Compensation Using Style-Transfer Synthesis in CT Images. In: International Workshop on Simulation and Synthesis in Medical Imaging: Springer, Cham; 2018. p.100-110.
     

  2. Kim S, Jang Y, Jeon B, Hong Y, Shim H, & Chang H. Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. In: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Springer, Cham; 2018. p.161-168.
     

  3. Sang-Eun Lee, Christopher Nguyen, Jongjin Yoon, Hyuk-Jae Chang, Sekeun Kim, Chul Hoon Kim, Debiao Li. “Three-dimensional Cardiomyocytes Structure Revealed By Diffusion Tensor Imaging and Its Validation Using a Tissue-Clearing Technique” Scientific reports, 2018
     

  4. Sang-Wook Lee Hyung-Bok Park, Yeonggul Jang et al. “Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System: Comparison with Invasive Fractional Flow Reserve” Journal of Cardiovascular Computed Tomography, 2018
     

  5. Yeonggul Jang, et al. “Full Quantification of Left Ventricle using Deep Multitask Network with Com-bination of 2D and 3D Convolution on 2D + t cine MRI”, International Workshop on Statistical At-lases and Computational Models of the Heart in conjunction with MICCAI 2018.

2017

  1. Byunghwan Jeon, Yoonmi Hong, Dongjin Han, Yeonggul Jang, et al. “Maximum a posteriori estima-tion method for aorta localization and coronary seed identification.” Pattern Recognition 68 (2017): 222-232.
     

  2. Hong Y, Hong Y-M, Jang Y, et al. “Coronary luminal and wall mask prediction using convolutional neural network” International Society of Biomedical Image 2017 proceeding.
     

  3. Sang Jin Ha, Yeonggul Jang, et al. “Assessment of myocardial viability based on dual-energy com-puted tomography in patients with chronic myocardial infarction: comparison with magnetic reso-nance imaging.” Clinical imaging 46 (2017): 8-13.

2016

  1. Na, D., Hong, S. J., Yoon, M. A., Ahn, K. S., Kang, C. H., Kim, B. H., & Jang, Y. (2016). Spinal bone bruise: can computed tomography (CT) enable accurate diagnosis?. Academic radiology, 23(11), 1376-1383.
     

  2. Dongjin Han, Hackjoon Shim, Byunghwan Jeon, Yeonggul Jang, et al. "Automatic coronary artery segmentation using active search for branches and seemingly disconnected vessel segments from cor-onary CT angiography." PloS one 11.8 (2016): e0156837.
     

  3. Guanglei Xiong, Peng Sun, Anna Starikov, Haoyin Zhou, Seongmin Ha, Quynh Truong, and James K. Min, “Comprehensive Modeling and Visualization of Cardiac Anatomy and Physiology from CT Imaging and Computer Simulation”, IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
     

  4. Zhou, H., Sun, P., Ha, S., Min, J. K., & Xiong, G. (2016). Modeling of Bifurcated Tubular Structures for Vessel Segmentation. In Computational Biomechanics for Medicine (pp. 187-194). Springer, Cham.
     

  5. Jang Y, Kim D, Jeon B, Han D, Shim H, & Chang H. Generation of Triangular Mesh of Coronary Artery Using Mesh Merging. Journal of KIISE 2016; 43(4):419-429.

  6. Jang, Y. Jung H. Y, Hong Y, et al. "Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images." Computational Mathematical Methods in Medicine 2016; 4561979.
     

  7. Jeon B, Jang Y, Han D, Shim H, Park H, & Chang H. Vessel Tracking Algorithm using Multiple Local Smooth Paths. Journal of the Institute of Electronics and Information Engineers 2016; 53(6):137-145.
     

  8. Ryu, Y. J., Choi, Y. H., Cheon, J. E., Ha, S., Kim, W. S., & Kim, I. O. (2016). Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom. Pediatric radiology, 46(3), 303-315.

2015

  1. Chung H, Jeon B (co-first author), Chang H, Han D, Shim H, Cho I, ... & Chung N. Predicting peri-device leakage of left atrial appendage device closure using novel three-dimensional geometric CT analysis. Journal of cardiovascular ultrasound 2015; 23(4):211-218.
     

  2. Ha, Seongmin & Jung, Sunghee & Chang, Hyuk-Jae & Park, Eun-Ah & Shim, Hackjoon. (2015). Effects of Iterative Reconstruction Algorithm, Automatic Exposure Control on Image Quality, and Radiation Dose: Phantom Experiments with Coronary CT Angiography Protocols. Progress in Medical Physics. 26. . 10.14316/pmp.2015.26.1.28.
     

  3. Hong Y, Shin S, Park HB, et al. Feasibility of Selective Catheter-Directed Coronary Computed Tomography Angiography Using Ultralow-Dose Intracoronary Contrast Injection in a Swine Model. Investigative radiology. 2015; 50(7): 449-455.
     

  4. Jang, Y. Cho I, Hartaigh B. W., et al. "Viability assessment after conventional coronary angiography using a novel cardiovascular interventional therapeutic CT system: Comparison with gross morphology in a subacute infarct swine model." Journal of Cardiovascular Computed Tomography. 2015; 9(4): 321-328.
     

  5. Jung, Sunghee & Lee, Soochahn & Shim, Hackjoon & Jung, Ho Yub & Heo, Yong & Chang, Hyuk-Jae. (2015). An Automatic Algorithm for Vessel Segmentation in X-Ray Angiogram using Random Forest. Journal of Biomedical Engineering Research. 36. 79-85. 10.9718/JBER.2015.36.4.79.
     

  6. Saebeom Hur, Hwan Jun Jae, Yeonggul Jang, et al. “Quantitative assessment of foot blood flow by using dynamic volume perfusion CT technique: a feasibility study.” Radiology 279.1 (2015): 195-206.

2014

  1. Han D, Doan N, Shim H, Jeon B, Lee H, Hong Y, & Chang H. A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA. Computer methods and programs in biomedicine 2014; 117(2):179-188.

2013

  1. Seongmin Ha, Hyuk-Jae Chang, Seonkyu Kim and Hackjoon Shim*, "Quantitative evaluation of iterative reconstruction algorithm for high quality computed tomography image acquisition with low-dose radiation: Comparison with filtered back projection algorithm", The Korean Society of Broadcast Engineers (KOSBE), June. 2013.
     

  2. Youngtaek Hong, Hyuk-Jae Chang*, Sanghoon Shin, Seongmin Ha, Se-Il Park, Sun-Mi Choi, Il-Chang Na, Hackjoon Shim and Yangsoo Jang, "Selective Catheter-directed Coronary Computed Tomography Angiography", The Korean Society of Cardiology, 2013

2012

  1. Cho I, Shim J, Chang HJ, et al. Prognostic value of multidetector coronary computed tomography angiography in relation to exercise electrocardiogram in patients with suspected coronary artery disease. Journal of the American College of Cardiology. 2012; 60(21):2205-15
     

  2. Yeonggul Jang, et al. “Core-Shell Detection in Images of Polymer Microbeads.” Computer Applications for Bio-technology, Multimedia, and Ubiquitous City. Springer, Berlin, Heidelberg, 2012. 9-15.

2011

  1. Hyunjoon Lee, Youngtaek Hong, Hackjoon Shim, Dongjin Kwon, Il Dong Yun,Sang Uk Lee, Namkug Kim and Joon Beom Seo “Feature-based Non-rigid Registration between Pre- and Post-Contrast Lung CT Images”, Journal of Biomedical Engineering Research. 2011; 32(3): 237-244, 2011
     

  2. Yeonggul Jang, et al. "Mask-Rendering of Mitochondrial Transports Using VTK." Database Theory and Application, Bio-Science and Bio-Technology. Springer, Berlin, Heidelberg, 2011. 161-166.
     

  3. Yeonggul Jang, et al. "Volume-Rendering of Mitochondrial Transports Using VTK." International Conference on Advanced Software Engineering and Its Applications. Springer, Berlin, Heidelberg, 2011.

Conferences

2024

  1. Jaeik Jeon, Jiyeon Kim, et al.,"A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Dopler Echocardiography", IEEE International Symposium on Biomedical Imaging 2024 (ISBI24), May 27, 2024.

2020

  1. Byungwhan Jeon, et al.,"강화학습기반 3차원 의료영상에서 물체추적을 위한 공간-순서에 완전 명시적 인코딩 네트워크", 32th Workshop on Image Processing and Image Understanding (IPIU), Feb 7, 2020.
     

  2. Kyunghoon Han, et al., "2D X-선 영상에서 관상동맥의 유실부위의 지역적 분석을 통한 강건한 재연결 기법", 32th Workshop on Image Processing and Image Understanding (IPIU), Feb 6, 2020.
     

  3. Gaeun Kim, et al., "CT 영상에서 좌심실 심근 자동 분할을 위한 딥러닝 모델의 성능 비교", 32th Workshop on Image Processing and Image Understanding (IPIU), Feb 6, 2020.

2019

  1. Jang, Yeonggul & Hong, Youngtaek & Jung, Sunghee & Shim, Hackjoon & Chang, Hyuk-Jae. (2019). Final Diagnosis Classification using Fine-tune BERT for Automatic Labeling. Journal of the Institute of Electronics and Information Engineers. 56. 92-98. 10.5573/ieie.2019.56.12.92.
     

  2. Kyunghoon Han, Jeon B. et al., “Coronary Artery Segmentation Method Using Local Patch-based Re-correction in 2D X-ray Images”, 31th Workshop on Image Processing and Image Understanding (IPIU), Feb 14, 2019.
     

  3. Kyeongjin Ann, Jang Y, et al., “Generation of High-Resolution Chest X-rays using Multi-scale Conditional Generative Adversarial Network with At-tention”, 31th Workshop on Image Processing and Image Understanding (IPIU), Feb 14, 2019.
     

  4. Chanhee Park, Jina Lee, et al, ComDia+: An Interactive Visual analytics System for Comparing, Diagnosing, and Improving Multiclass Classifiers, PacificVis (2019).
     

  5. Sekeun Kim, Kyunghoon Han, et al., “A Cascaded Two-step Approach For Segmentation of Thoracic Organs”, IEEE International Symposium on Biomedical Imaging (ISBI 2019).
     

  6. Hyunseok Jeong, et al., “Deep learning algorithm for skin lesion severity classification based on lesion information”, Korea Computer Congress 2019(KCC2019).
     

  7. Kyunghoon Han, Jeon B, et al., “Merging Method of Vessel Segments in X-ray/CT Images Using Analysis of Geometric -Relation”, The Institute of Electronics and Information Engineers (IEIE), Jun 28, 2019.
     

  8. Gaeun Kim et al., “A study on the Emotion Analysis of Facial Expressions for Development of Psychiatric Diagnostic Tool”, The Korean Society of Medical & Biological Engineering, May 10, 2019.
     

  9. 이지나 외, Wasserstein 생산적 적대 신경망과 구조적 유사지수를 이용한 저선량 컴퓨터 단층촬영 영상 잡음 제거 기법, 한국정보과학회 (2019).

2018

  1. Jung, S., Lee, S., Jeon, B., Jang, Y., & Chang, H. J. (2018, September). Deep Learning Based Coronary Artery Motion Artifact Compensation Using Style-Transfer Synthesis in CT Images. In International Workshop on Simulation and Synthesis in Medical Imaging (pp. 100-110). Springer, Cham.
     

  2. Jang, Y., Kim, S., Shim, H., & Chang, H. J. (2018, September). Full Quantification of Left Ventricle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D+ t Cine MRI. In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 476-483). Springer, Cham.
     

  3. S Kim, Y Jang, B Jeon, Y Hong, H Shim, H Chang. “Fully Automatic Segmentation of Coronary Arteries based on Deep Neural Network in Intravascular Ultrasound Images” In MICCAI 2018 Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT). LNCS, MICCAI Workshop, 2018.
     

  4. The International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) Sub-Challenge Gastrointestinal Image ANAlysis (GIANA) Part of the Endoscopic Vision Challenge, Sep 9, 2018.
     

  5. Kyeongjin Ann, Jang Y, et al., “Extraction of the Final Diagnosis from Medical Treatment Record Based on Deep Learning for the Automatic Labeling”, The Institute of Electronics and Information Engineers (IEIE), Jun 29, 2018.
     

  6. S. Kim,Y. Jang,S. Ha Y-M. Hong, H. Shim, H. J. Chang. “Automatic Segmentation of Coronary Arteries in Intra-vascular Ultrasound Images using Convolutional Neural Networks in Polar Coordinates” IEEE International Symposium on Biomedical Imagaing(ISBI 2018).
     

  7. 전병환 외, 관상동맥 검출을 위한 베이지안 접근방법. 대한전자공학회 학술대회, 455-456. (2018).
     

  8. 한경훈 외, 기하학관계 분석을 통한 X-ray/CT 영상 기반 혈관 조각 병합기법. 대한전자공학회 학술대회, 617-620 (2018).

2017

  1. Y Hong, YM Hong, Y Jang, S Kim, B Jeon, S Jung, et al. “Coronary luminal and wall mask prediction using convolutional neural network” Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on. IEEE, 2017.
     

  2. Jang, Y., Hong, Y., Ha, S., Kim, S., & Chang, H. J. (2017, September). Automatic segmentation of LV and RV in cardiac MRI. In International Workshop on Statistical Atlases and Computational Models of the Heart (pp. 161-169). Springer, Cham.
     

  3. 김세근, 장영걸, 하성민, 심학준, 장혁재. “딥러닝 기반 혈관내 초음파 영상(IVUS)에서의 혈관 분할 기법” 순환기의공학회 (2017. 12. 16)
     

  4. 전병환 외, Left Atrium Extraction from Computed Tomography Angiography for Simulation of Left Atrial Appendage Occlusion, 순환기의공학회 (2017. 12. 16)

2016

  1. Yeonggul Jang, Iksung Cho, Seongmin Ha, et al., “Assessment of Myocardial Viability using a Novel Cardiovascular Interventional Therapeutic Computed Tomography (CVIT-CT) System in Patients with Chronic Myocardial Infarction (CMI): Comparison with Magnetic Resonance Imaging”, 60th Annual Scientific Meeting of the Korean Society of Cardiology, Nov 25, 2016.
     

  2. Y Hong et al., “Novel Protocol for Catheter-Direct Injection based Coronary Computed Tomography Angiography”, 28th Workshop on Image Processing and Image Understanding, Feb 02, 2016.
     

  3. Yeonggul Jang, et al., “Geodesic Distance Algorithm for Ascending Aorta Segmentation in Coronary Computed Tomography Angiography”, 28th Workshop on Image Processing and Image Understanding, 2016.
     

  4. 김세근, 장영걸, 하성민, 심학준, 장혁재. “관상동맥 이종영상(2D-3D) 정합의 고속화를 위한 방사선 불투과성 입체표지”, 28th Workshop on Image Processing and Image Understanding, 2016.
     

  5. Yeonggul Jang, et al., “Generation of Triangular Mesh of Coronary Artery Using Mesh Merging”, Biomedical Engineering Society for Circulation, 2016
     

  6. Sekeun Kim, Yeonggul Jang, Byunghwan Jeon, Youngtaek Hong, Hackjoon Shim, Hyukjae Chang. “Visualization of Myocardial Fiber in the Pig Heart from Diffusion Tensor Magnetic Resonance Imaging (DTMRI)” Biomedical Engineering Society For Circulation, 2016.
     

  7. 전병환 외, Novel Three-Dimensional Geometric Parameters for Predicting the Incomplete Occlusion of Left Atrial Appendage Closure, 순환기의공학회 (2016. 12. 3)
     

  8. 전병환 외, Pre-processing for Fully Automatic Coronary Artery Segmentation: Aorta Localization and Ostia Detection, 순환기의공학회 (2016. 5. 27)

2015

  1. Yeonggul Jang, et al., “Viability Assessment after Conventional Coronary Angiography Using a Novel Cardiovascular Interventional Therapeutic Computed Tomography (CVIT-CT) System: Comparison with Gross Morphology in a Sub-Acute Infarct Swine Model”, Society of Cardiovascular Computed Tomography 2015 Annual Scientific Meeting.
     

  2. 전병환 외, Coronary Artery Extraction and Registration between 3D CCTA and 2D XA for Image Guided Intervention, 순환기의공학회 (2015. 12. 19)
     

  3. 전병환 외, Coronary Artery Tracking and its Organization from CCTA, 순환기의공학회 (2015. 7. 4)

2014

  1. Youngtaek Hong, Sanghoon Shin, Hyuk-Jae Chang, Hyung-Bok Park, Seongmin Ha, Se-Il Park, Ji Min Sung, Hackjoon Shim, Yangsoo Jang, Namsik Chung., “Feasibility of Selective Catheter-Directed Coronary Computed Tomography Angiography Using Ultra-Low-Dose Intracoronary Contrast Injection in a Swine Model”, 58th Annual Scientific Meeting of the Korean Society of Cardiology, Nov 28-29, 2014.
     

  2. Hyung-Bok Park, Sang-Hoon Shin, Hyuk Jeon, Youngtaek Hong, In-Jeong Cho, James K. Min, Hyuk-Jae Chang, “Comparison of Image Quality between Selective Intracoronary Contrast Injected Coronary Computed Tomography Angiography and Conventional Intravenous Contrast Injected Coronary Computed Tomography Angiography”, 9th Annual Scientific Meeting July 10-13, 2014.

2013

  1. Youngtaek Hong, Hyuk-Jae Chang, Sanghoon Shin, Seongmin Ha, Se-Il Park, Sun-Mi Choi, Il-Chang Na1, Hackjoon Shim, Yangsoo Jang, “Selective Catheter-directed Coronary Computed Tomography Angiography”, 57th Annual Scientific Meeting of the Korean Society of Cardiology, Nov 29-30, 2013.

2012

  1. Youngtaek Hong, Sang-Hoon Shin, Hackjoon Shim, & Hyuk-Jae Chang, “Optimization of Contras Injection Protocol for Selective Coronary CTA of 640-slice CT Using Trans-luminal Attenuation Gradient (TAG): Initial Experiences in Swine Model”, 56th Annual Scientific Meeting of the Korean Society of Cardiology, Nov 16, 2012.

Conferences
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