Authors: Steve B. Jiang, Dan Nguyen, Chenyang Shen, Fan-Chi F. Su, Jiacheng Xie, Shunyu Yan, Daniel Yang, Ying Zhang, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas
Abstract Preview: Purpose: Accurate delineation of treatment targets and organs-at-risk is crucial for radiotherapy. Despite significant progress in artificial intelligence (AI)-based automatic segmentation tools, effi...
Authors: Hector Andrade-Loarca, Ines Butz, Chiara Gianoli, Prof. Gitta Kutyniok, Jianfei Li, Katia Parodi, Prof. Vincenzo Patera, Angelo Schiavi, Prof. Ozan Öktem
Affiliation: Sapienza University of Rome, Department of Mathematics, Royal Institute of Technology, School of Computation, Information and Technology, Technische Universitaet Muenchen, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Department of Mathematics, Ludwig-Maximilians-Universität (LMU) München, Department of Medical Physics, Ludwig-Maximilians-Universität (LMU) München
Abstract Preview: Purpose: To explore and demonstrate the feasibility of accurate and fast prediction of the water equivalent thickness (WET) distribution of tissue traversed by a proton imaging pencil beam, aiming at ...
Authors: Mark Anastasio, Hua Li, Zhuchen Shao
Affiliation: Washington University School of Medicine, University of Illinois Urbana-Champaign
Abstract Preview: Purpose: Automated semantic segmentation of cell nuclei in microscopic images is vital for disease diagnosis and tissue microenvironment analysis. However, obtaining large annotated datasets for train...