Authors: Kristen A. Duke, Samer Jabor, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia
Affiliation: St. Mary's University, The University of Texas San Antonio, UT Health San Antonio
Abstract Preview: Purpose:
The Segment Anything Model (SAM) is a foundational box-prompt-based model for natural image segmentation. However, its applicability to zero-shot 3D medical image segmentation, particularl...
Authors: Jihun Kim, Jin Sung Kim, Jun Won Kim, Yong Tae Kim, Chanwoong Lee, Jihyn Pyo, Young Hun Yoon
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine
Abstract Preview: Purpose: Although segmentation foundation models have recently demonstrated promising zero-shot performance on natural images, its clinical application to magnetic resonance (MR) images still requires...