Prof. Hyoungseop Kim Profile

Prof. Hyoungseop Kim

at Kyushu Institute of Technology

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Author

Publications (9)

Proceedings Article | 23 December 2022 Paper

Recognition of sidewalk environment based on WideSegPlus

Yuki Saikai, Huimin Lu, Yujie Li, Tohru Kamiya

Proceedings Volume 12508, 1250802 (2022) https://doi.org/10.1117/12.2655680

KEYWORDS: Reliability, Image segmentation, Convolution, Algorithm development, Detection and tracking algorithms, Image processing algorithms and systems, Image processing, Convolutional neural networks, Video, Object recognition

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Proceedings Article | 17 March 2020 Paper

GAN-based survival prediction model from CT images of patients with idiopathic pulmonary fibrosis

Tomoki Uemura, Chinatsu Watari, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11318, 113181F (2020) https://doi.org/10.1117/12.2551369

KEYWORDS: Computed tomography, Chest, Pulmonary fibrosis, Performance modeling, Network architectures, Physiology, Lung, Biological research, Image analysis, 3D modeling

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We developed a novel survival prediction model for images, called pix2surv, based on a conditional generative adversarial network (cGAN), and evaluated its performance based on chest CT images of patients with idiopathic pulmonary fibrosis (IPF). The architecture of the pix2surv model has a time-generator network that consists of an encoding convolutional network, a fully connected prediction network, and a discriminator network. The fully connected prediction network is trained to generate survival-time images from the chest CT images of each patient. The discriminator network is a patchbased convolutional network that is trained to differentiate the “fake pair” of a chest CT image and a generated survivaltime image from the “true pair” of an input CT image and the observed survival-time image of a patient. For evaluation, we retrospectively collected 75 IPF patients with high-resolution chest CT and pulmonary function tests. The survival predictions of the pix2surv model on these patients were compared with those of an established clinical prognostic biomarker known as the gender, age, and physiology (GAP) index by use of a two-sided t-test with bootstrapping. Concordance index (C-index) and relative absolute error (RAE) were used as measures of the prediction performance. Preliminary results showed that the survival prediction by the pix2surv model yielded more than 15% higher C-index value and more than 10% lower RAE values than those of the GAP index. The improvement in survival prediction by the pix2surv model was statistically significant (P < 0.0001). Also, the separation between the survival curves for the low- and high-risk groups was larger with pix2surv than that of the GAP index. These results show that the pix2surv model outperforms the GAP index in the prediction of the survival time and risk stratification of patients with IPF, indicating that the pix2surv model can be an effective predictor of the overall survival of patients with IPF.

Proceedings Article | 16 March 2020 Paper

U-radiomics for predicting survival of patients with idiopathic pulmonary fibrosis

Tomoki Uemura, Chinatsu Watari, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 113140T (2020) https://doi.org/10.1117/12.2551273

KEYWORDS: Computed tomography, Lung, Pulmonary fibrosis, Chest, Performance modeling

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Proceedings Article | 16 March 2020 Paper

Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography

Janne Näppi, Tomoki Uemura, Se Hyung Kim, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 113143C (2020) https://doi.org/10.1117/12.2549793

KEYWORDS: Spectral computed tomography, Machine learning, Computed tomography, Cancer, Computer aided diagnosis and therapy, Virtual colonoscopy, Colorectal cancer, 3D image processing, Radiology

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Proceedings Article | 16 March 2020 Paper

Comparative performance of 3D-DenseNet, 3D-ResNet, and 3D-VGG models in polyp detection for CT colonography

Tomoki Uemura, Janne Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida

Proceedings Volume 11314, 1131435 (2020) https://doi.org/10.1117/12.2549103

KEYWORDS: 3D modeling, Computer aided diagnosis and therapy, 3D image processing, Medical imaging, Performance modeling, Computed tomography, Virtual colonoscopy, Visualization, Data modeling, Solid modeling

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Proceedings Article | 18 March 2019 Presentation + Paper

Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography

Tomoki Uemura, Janne Näppi, Huimin Lu, Hyoungseop Kim, Rie Tachibana, Toru Hironaka, Hiroyuki Yoshida

Proceedings Volume 10950, 1095013 (2019) https://doi.org/10.1117/12.2512173

KEYWORDS: Performance modeling, Virtual colonoscopy, Data modeling, Statistical modeling, Gallium nitride, Computed tomography, Image processing, Convolution, 3D modeling, Computer aided diagnosis and therapy

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Proceedings Article | 4 March 2015 Paper

Groupwise surface correspondence using particle filtering

Guangxu Li, Hyoungseop Kim, Joo Kooi Tan, Seiji Ishikawa

Proceedings Volume 9443, 94432O (2015) https://doi.org/10.1117/12.2179122

KEYWORDS: Particle filters, 3D modeling, Spherical lenses, Particles, Statistical modeling, Lung, Data modeling, Statistical analysis, Image segmentation, Optical spheres

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Proceedings Article | 17 March 2008 Paper

A new registration method with voxel-matching technique for temporal subtraction images

Yoshinori Itai, Hyoungseop Kim, Seiji Ishikawa, Shigehiko Katsuragawa, Kunio Doi

Proceedings Volume 6915, 69153I (2008) https://doi.org/10.1117/12.769921

KEYWORDS: Lung, Computed tomography, Image registration, Image enhancement, Medical imaging, 3D image processing, Image segmentation, Computer aided diagnosis and therapy, Medical research, Blood vessels

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Proceedings Article | 30 March 2007 Paper

3D temporal subtraction on multislice CT images using nonlinear warping technique

Takayuki Ishida, Shigehiko Katsuragawa, Ikuo Kawashita, Hyounseop Kim, Yoshinori Itai, Kazuo Awai, Qiang Li, Kunio Doi

Proceedings Volume 6514, 65143I (2007) https://doi.org/10.1117/12.709004

KEYWORDS: Computed tomography, 3D image processing, Lung, 3D image enhancement, Image segmentation, Image registration, Heart, Computer aided diagnosis and therapy, Chest imaging, Radiology

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Showing 5 of 9 publications

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