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361 results on '"Cho, Jaeyoung"'

1. The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

Author

de Verdier, Maria Correia, Saluja, Rachit, Gagnon, Louis, LaBella, Dominic, Baid, Ujjwall, Tahon, Nourel Hoda, Foltyn-Dumitru, Martha, Zhang, Jikai, Alafif, Maram, Baig, Saif, Chang, Ken, D'Anna, Gennaro, Deptula, Lisa, Gupta, Diviya, Haider, Muhammad Ammar, Hussain, Ali, Iv, Michael, Kontzialis, Marinos, Manning, Paul, Moodi, Farzan, Nunes, Teresa, Simon, Aaron, Sollmann, Nico, Vu, David, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Chai, Rongrong, Chung, Verena, Faghani, Shahriar, Farahani, Keyvan, Kazerooni, Anahita Fathi, Iglesias, Eugenio, Kofler, Florian, Li, Hongwei, Linguraru, Marius George, Menze, Bjoern, Moawad, Ahmed W., Velichko, Yury, Wiestler, Benedikt, Altes, Talissa, Basavasagar, Patil, Bendszus, Martin, Brugnara, Gianluca, Cho, Jaeyoung, Dhemesh, Yaseen, Fields, Brandon K. K., Garrett, Filip, Gass, Jaime, Hadjiiski, Lubomir, Hattangadi-Gluth, Jona, Hess, Christopher, Houk, Jessica L., Isufi, Edvin, Layfield, Lester J., Mastorakos, George, Mongan, John, Nedelec, Pierre, Nguyen, Uyen, Oliva, Sebastian, Pease, Matthew W., Rastogi, Aditya, Sinclair, Jason, Smith, Robert X., Sugrue, Leo P., Thacker, Jonathan, Vidic, Igor, Villanueva-Meyer, Javier, White, Nathan S., Aboian, Mariam, Conte, Gian Marco, Dale, Anders, Sabuncu, Mert R., Seibert, Tyler M., Weinberg, Brent, Abayazeed, Aly, Huang, Raymond, Turk, Sevcan, Rauschecker, Andreas M., Farid, Nikdokht, Vollmuth, Philipp, Nada, Ayman, Bakas, Spyridon, Calabrese, Evan, and Rudie, Jeffrey D.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

Published
2024

2. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Author

Moawad, Ahmed W., Janas, Anastasia, Baid, Ujjwal, Ramakrishnan, Divya, Saluja, Rachit, Ashraf, Nader, Jekel, Leon, Amiruddin, Raisa, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Aneja, Sanjay, Anwar, Syed Muhammad, Bergquist, Timothy, Calabrese, Evan, Chiang, Veronica, Chung, Verena, Conte, Gian Marco Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kofler, Florian, Krantchev, Kiril, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Link, Katherine E., Liu, Xinyang, Maleki, Nazanin, Meier, Zeke, Menze, Bjoern H, Moy, Harrison, Osenberg, Klara, Piraud, Marie, Reitman, Zachary, Shinohara, Russel Takeshi, Tahon, Nourel hoda, Nada, Ayman, Velichko, Yuri S., Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter, Shafique, Umber, Willms, Klara, Avesta, Arman, Bousabarah, Khaled, Chakrabarty, Satrajit, Gennaro, Nicolo, Holler, Wolfgang, Kaur, Manpreet, LaMontagne, Pamela, Lin, MingDe, Lost, Jan, Marcus, Daniel S., Maresca, Ryan, Merkaj, Sarah, Nada, Ayaman, Pedersen, Gabriel Cassinelli, von Reppert, Marc, Sotiras, Aristeidis, Teytelboym, Oleg, Tillmans, Niklas, Westerhoff, Malte, Youssef, Ayda, Godfrey, Devon, Floyd, Scott, Rauschecker, Andreas, Villanueva-Meyer, Javier, Pflüger, Irada, Cho, Jaeyoung, Bendszus, Martin, Brugnara, Gianluca, Cramer, Justin, Perez-Carillo, Gloria J. Guzman, Johnson, Derek R., Kam, Anthony, Kwan, Benjamin Yin Ming, Lai, Lillian, Lall, Neil U., Memon, Fatima, Patro, Satya Narayana, Petrovic, Bojan, So, Tiffany Y., Thompson, Gerard, Wu, Lei, Schrickel, E. Brooke, Bansal, Anu, Barkhof, Frederik, Besada, Cristina, Chu, Sammy, Druzgal, Jason, Dusoi, Alexandru, Farage, Luciano, Feltrin, Fabricio, Fong, Amy, Fung, Steve H., Gray, R. Ian, Ikuta, Ichiro, Iv, Michael, Postma, Alida A., Mahajan, Amit, Joyner, David, Krumpelman, Chase, Letourneau-Guillon, Laurent, Lincoln, Christie M., Maros, Mate E., Miller, Elka, Morón, Fanny, Nimchinsky, Esther A., Ozsarlak, Ozkan, Patel, Uresh, Rohatgi, Saurabh, Saha, Atin, Sayah, Anousheh, Schwartz, Eric D., Shih, Robert, Shiroishi, Mark S., Small, Juan E., Tanwar, Manoj, Valerie, Jewels, Weinberg, Brent D., White, Matthew L., Young, Robert, Zohrabian, Vahe M., Azizova, Aynur, Brüßeler, Melanie Maria Theresa, Fehringer, Pascal, Ghonim, Mohanad, Ghonim, Mohamed, Gkampenis, Athanasios, Okar, Abdullah, Pasquini, Luca, Sharifi, Yasaman, Singh, Gagandeep, Sollmann, Nico, Soumala, Theodora, Taherzadeh, Mahsa, Yordanov, Nikolay, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Malhotra, Ajay, Abayazeed, Aly H., Dellepiane, Francesco, Lohmann, Philipp, Pérez-García, Víctor M., Elhalawani, Hesham, Al-Rubaiey, Sanaria, Armindo, Rui Duarte, Ashraf, Kholod, Asla, Moamen M., Badawy, Mohamed, Bisschop, Jeroen, Lomer, Nima Broomand, Bukatz, Jan, Chen, Jim, Cimflova, Petra, Corr, Felix, Crawley, Alexis, Deptula, Lisa, Elakhdar, Tasneem, Shawali, Islam H., Faghani, Shahriar, Frick, Alexandra, Gulati, Vaibhav, Haider, Muhammad Ammar, Hierro, Fátima, Dahl, Rasmus Holmboe, Jacobs, Sarah Maria, Hsieh, Kuang-chun Jim, Kandemirli, Sedat G., Kersting, Katharina, Kida, Laura, Kollia, Sofia, Koukoulithras, Ioannis, Li, Xiao, Abouelatta, Ahmed, Mansour, Aya, Maria-Zamfirescu, Ruxandra-Catrinel, Marsiglia, Marcela, Mateo-Camacho, Yohana Sarahi, McArthur, Mark, McDonnell, Olivia, McHugh, Maire, Moassefi, Mana, Morsi, Samah Mostafa, Muntenu, Alexander, Nandolia, Khanak K., Naqvi, Syed Raza, Nikanpour, Yalda, Alnoury, Mostafa, Nouh, Abdullah Mohamed Aly, Pappafava, Francesca, Patel, Markand D., Petrucci, Samantha, Rawie, Eric, Raymond, Scott, Roohani, Borna, Sabouhi, Sadeq, Sanchez-Garcia, Laura M., Shaked, Zoe, Suthar, Pokhraj P., Altes, Talissa, Isufi, Edvin, Dhermesh, Yaseen, Gass, Jaime, Thacker, Jonathan, Tarabishy, Abdul Rahman, Turner, Benjamin, Vacca, Sebastiano, Vilanilam, George K., Warren, Daniel, Weiss, David, Worede, Fikadu, Yousry, Sara, Lerebo, Wondwossen, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Bakas, Spyridon, Rudie, Jeffrey D., and Aboian, Mariam

Subjects
Quantitative Biology - Other Quantitative Biology, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Published
2023

13. Application of Generative Design in Social Manufacturing

Author

Votintseva, Anjelika, Johnson, Rebecca, Zabigailo, Maryna, Cho, Jaeyoung, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Stephanidis, Constantine, editor, Antona, Margherita, editor, and Ntoa, Stavroula, editor

Published
2022
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15. MP36-18 ARTIFICIAL INTELLIGENCE BASED PERSONALIZED ONCOLOGICAL OUTCOME PREDICTION MODEL FOR UPPER URINARY TRACT UROTHELIAL CARCINOMA AFTER RADICAL NEPHROURETERECTOMY: DEVELOPMENT AND MULTI-CENTER VALIDATION

Author

Kang, Homin, primary, Cho, Jaeyoung, additional, Lim, Bumjin, additional, You, Dalsan, additional, Song, Cheryn, additional, Jeong, In Gab, additional, Hong, Bumsik, additional, Hong, Jun Hyuk, additional, Ahn, Hanjong, additional, Kim, Hwanik, additional, and Ku, Ja Hyeon, additional

Published
2024
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37. Drone Relay Stations for Supporting Wireless Communication in Military Operations

Author

Kim, Seon Jin, Lim, Gino J., Cho, Jaeyoung, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, and Chen, Jessie, editor

Published
2018
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40. Impact of the COVID-19 pandemic on adherence to positive airway pressure treatment in patients with obstructive sleep apnea: systematic review and meta-analysis.

Author

Jo, Soomin and Cho, Jaeyoung

Subjects
SLEEP apnea syndromes, COVID-19 pandemic, PATIENT compliance, COVID-19, AIRWAY (Anatomy)
Abstract

Background: The adverse effects of the COVID-19 pandemic on sleep have been well studied. However, whether the COVID-19 pandemic impacted positive airway pressure (PAP) adherence in patients with obstructive sleep apnea (OSA) remains unclear. Objectives: To evaluate the impact of the COVID-19 pandemic on adherence to PAP therapy in patients with OSA. Design: A retrospective cohort study, systematic review, and meta-analysis. Data sources and methods: The retrospective study included adults with OSA who received PAP prescriptions within the year before and the year after the start date of COVID-19 social distancing (22 March 2020) in South Korea. The threshold of clinical significance for PAP adherence was defined as 0.5 h/day. We also searched for relevant studies published up to 15 January 2023 using the MEDLINE and Embase databases. We performed a random-effects meta-analysis of our findings with the identified studies regarding the standardized mean change (SMC) with a 95% CI of PAP adherence. Results: Our study included a total of 306 patients (mean age, 59.7 years; men, 73.5%). The average daily PAP usage was 5.10 ± 1.63 h before the COVID-19 pandemic and 4.79 ± 1.96 h during the pandemic (mean difference, −0.31 h/day; 95% CI, −0.46 to −0.15 h/day; p < 0.001 using paired t -test). After identifying 10 observational before-and-after studies through a systematic review, we conducted a meta-analysis that included our original data and showed that adherence to PAP treatment was not different before and during the COVID-19 pandemic (SMC, 0.01; 95% CI, −0.18 to 0.19; p = 0.952). Conclusion: Although the COVID-19 pandemic had a statistically negative impact on adherence to PAP treatment in South Korea, the effect was not clinically relevant in patients with OSA. According to our meta-analysis, adherence to PAP treatment was not different before and during the COVID-19 pandemic in patients with OSA. Trial registration: PROSPERO database; No.: CRD42023414268; URL: https://www.crd.york.ac.uk/PROSPERO. [ABSTRACT FROM AUTHOR]

Published
2023
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