no code implementations • 5 May 2024 • Chaojie Zhang, Shengjia Chen, Ozkan Cigdem, Haresh Rengaraj Rajamohan, Kyunghyun Cho, Richard Kijowski, Cem M. Deniz
A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI).
no code implementations • 29 Apr 2024 • Ozkan Cigdem, Shengjia Chen, Chaojie Zhang, Kyunghyun Cho, Richard Kijowski, Cem M. Deniz
A survival analysis model for predicting time-to-total knee replacement (TKR) was developed using features from medical images and clinical measurements.
2 code implementations • 29 Apr 2020 • Arjun D. Desai, Francesco Caliva, Claudia Iriondo, Naji Khosravan, Aliasghar Mortazi, Sachin Jambawalikar, Drew Torigian, Jutta Ellermann, Mehmet Akcakaya, Ulas Bagci, Radhika Tibrewala, Io Flament, Matthew O`Brien, Sharmila Majumdar, Mathias Perslev, Akshay Pai, Christian Igel, Erik B. Dam, Sibaji Gaj, Mingrui Yang, Kunio Nakamura, Xiaojuan Li, Cem M. Deniz, Vladimir Juras, Ravinder Regatte, Garry E. Gold, Brian A. Hargreaves, Valentina Pedoia, Akshay S. Chaudhari
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
no code implementations • 20 Apr 2017 • Cem M. Deniz, Siyuan Xiang, Spencer Hallyburton, Arakua Welbeck, James S. Babb, Stephen Honig, Kyunghyun Cho, Gregory Chang
However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice.