Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Jaehee Chun"'
Autor:
Joonil Hwang, MS, Jaehee Chun, PhD, Seungryong Cho, PhD, Joo-Ho Kim, MS, Min-Seok Cho, MS, Seo Hee Choi, MD, Jin Sung Kim, PhD
Publikováno v:
Advances in Radiation Oncology, Vol 9, Iss 10, Pp 101580- (2024)
Purpose: Herein, we developed a deep learning algorithm to improve the segmentation of the clinical target volume (CTV) on daily cone beam computed tomography (CBCT) scans in breast cancer radiation therapy. By leveraging the Intentional Deep Overfit
Externí odkaz:
https://doaj.org/article/4058f51b951a409395f20db0c98c4872
Publikováno v:
Technical Innovations & Patient Support in Radiation Oncology, Vol 32, Iss , Pp 100273- (2024)
Due to anatomical changes between pre-planning and implantation, there exists a need for tools that can streamline the adjustment of needle and seed configurations in low dose rate brachytherapy for prostate cancer. Specifically, upon taking a second
Externí odkaz:
https://doaj.org/article/112c9040cfb34f9f828d9aa957514612
Autor:
Min Seo Choi, Jee Suk Chang, Kyubo Kim, Jin Hee Kim, Tae Hyung Kim, Sungmin Kim, Hyejung Cha, Oyeon Cho, Jin Hwa Choi, Myungsoo Kim, Juree Kim, Tae Gyu Kim, Seung-Gu Yeo, Ah Ram Chang, Sung-Ja Ahn, Jinhyun Choi, Ki Mun Kang, Jeanny Kwon, Taeryool Koo, Mi Young Kim, Seo Hee Choi, Bae Kwon Jeong, Bum-Sup Jang, In Young Jo, Hyebin Lee, Nalee Kim, Hae Jin Park, Jung Ho Im, Sea-Won Lee, Yeona Cho, Sun Young Lee, Ji Hyun Chang, Jaehee Chun, Eung Man Lee, Jin Sung Kim, Kyung Hwan Shin, Yong Bae Kim
Publikováno v:
Breast, Vol 74, Iss , Pp 103624- (2024)
Externí odkaz:
https://doaj.org/article/b89999eefe264fc38941988169939559
Autor:
Min Seo Choi, Jee Suk Chang, Kyubo Kim, Jin Hee Kim, Tae Hyung Kim, Sungmin Kim, Hyejung Cha, Oyeon Cho, Jin Hwa Choi, Myungsoo Kim, Juree Kim, Tae Gyu Kim, Seung-Gu Yeo, Ah Ram Chang, Sung-Ja Ahn, Jinhyun Choi, Ki Mun Kang, Jeanny Kwon, Taeryool Koo, Mi Young Kim, Seo Hee Choi, Bae Kwon Jeong, Bum-Sup Jang, In Young Jo, Hyebin Lee, Nalee Kim, Hae Jin Park, Jung Ho Im, Sea-Won Lee, Yeona Cho, Sun Young Lee, Ji Hyun Chang, Jaehee Chun, Eung Man Lee, Jin Sung Kim, Kyung Hwan Shin, Yong Bae Kim
Publikováno v:
Breast, Vol 73, Iss , Pp 103599- (2024)
Purpose: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potentia
Externí odkaz:
https://doaj.org/article/02c874daef2745c1a559234cccabbe4a
Autor:
Jaehee Chun, Jee Suk Chang, Caleb Oh, InKyung Park, Min Seo Choi, Chae-Seon Hong, Hojin Kim, Gowoon Yang, Jin Young Moon, Seung Yeun Chung, Young Joo Suh, Jin Sung Kim
Publikováno v:
Radiation Oncology, Vol 17, Iss 1, Pp 1-9 (2022)
Abstract Background Adjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the ca
Externí odkaz:
https://doaj.org/article/794a18705843423ea922f5afd10da37d
Autor:
Hwa Kyung Byun, Jee Suk Chang, Min Seo Choi, Jaehee Chun, Jinhong Jung, Chiyoung Jeong, Jin Sung Kim, Yongjin Chang, Seung Yeun Chung, Seungryul Lee, Yong Bae Kim
Publikováno v:
Radiation Oncology, Vol 16, Iss 1, Pp 1-8 (2021)
Abstract Purpose To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts. Methods Eleven experts from two institutions delineated nine OARs i
Externí odkaz:
https://doaj.org/article/6271a3efee73416d825437dfc3e439ef
Autor:
Seung Yeun Chung, Jee Suk Chang, Min Seo Choi, Yongjin Chang, Byong Su Choi, Jaehee Chun, Ki Chang Keum, Jin Sung Kim, Yong Bae Kim
Publikováno v:
Radiation Oncology, Vol 16, Iss 1, Pp 1-10 (2021)
Abstract Background In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) s
Externí odkaz:
https://doaj.org/article/a72e3e8987c74f15809ebe2054c7c8a6
Autor:
Jeongsu Park, Byoungsu Choi, Jaeeun Ko, Jaehee Chun, Inkyung Park, Juyoung Lee, Jayon Kim, Jaehwan Kim, Kidong Eom, Jin Sung Kim
Publikováno v:
Frontiers in Veterinary Science, Vol 8 (2021)
Purpose: This study was conducted to develop a deep learning-based automatic segmentation (DLBAS) model of head and neck organs for radiotherapy (RT) in dogs, and to evaluate the feasibility for delineating the RT planning.Materials and Methods: The
Externí odkaz:
https://doaj.org/article/77f4a431a15d43fbb5b1de3f18104e87
Autor:
Sang Kyun Yoo, Tae Hyung Kim, Jaehee Chun, Byong Su Choi, Hojin Kim, Sejung Yang, Hong In Yoon, Jin Sung Kim
Publikováno v:
Cancers, Vol 14, Iss 10, p 2555 (2022)
Recently, several efforts have been made to develop the deep learning (DL) algorithms for automatic detection and segmentation of brain metastases (BM). In this study, we developed an advanced DL model to BM detection and segmentation, especially for
Externí odkaz:
https://doaj.org/article/7f40cc16b6e24b1f8f7e2548bfd3db8d
Autor:
Juyoung Lee, Brian Bartholmai, Tobias Peikert, Jaehee Chun, Hojin Kim, Jin Sung Kim, Seong Yong Park
Publikováno v:
PLoS ONE, Vol 16, Iss 6, p e0253204 (2021)
Differentiating the invasiveness of ground-glass nodules (GGN) is clinically important, and several institutions have attempted to develop their own solutions by using computed tomography images. The purpose of this study is to evaluate Computer-Aide
Externí odkaz:
https://doaj.org/article/d014b98624ad4094bad3b1bf1762b4ed