ClickSAM: Fine-tuning Segment Anything Model using click prompts for ultrasound image segmentation
The Hockaday School（霍克黛学校）；Liberty High School（自由高中）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；The University of Texas Southwestern Medical Center at Dallas（德克萨斯大学达拉斯西南医学中心）
Aimee Guo, Grace Fei, Hemanth Pasupuleti, Jing Wang
The newly released Segment Anything Model (SAM) is a popular tool used in image processing due to its superior segmentation accuracy, variety of input prompts, training capabilities, and efficient model design. However, its current model is trained on a diverse dataset not tailored to medical images, particularly ultrasound images. Ultrasound images tend to have a lot of noise, making it difficult to segment out important structures. In this project, we developed ClickSAM, which fine-tunes the Segment Anything Model using click prompts for ultrasound images. ClickSAM has two stages of training: the first stage is trained on single-click prompts centered in the ground-truth contours, and the second stage focuses on improving the model performance through additional positive and negative click prompts. By comparing the first stage predictions to the ground-truth masks, true positive, false positive, and false negative segments are calculated. Positive clicks are generated using the true positive and false negative segments, and negative clicks are generated using the false positive segments. The Centroidal Voronoi Tessellation algorithm is then employed to collect positive and negative click prompts in each segment that are used to enhance the model performance during the second stage of training. With click-train methods, ClickSAM exhibits superior performance compared to other existing models for ultrasound image segmentation.
SAMJ: Fast Image Annotation on ImageJ/Fiji via Segment Anything Model
Bioengineering Department（生物工程系）；Universidad Carlos III de Madrid（卡洛斯三世大学）；Instituto de Investigación Sanitaria Gregorio Marañón（Gregorio Marañón卫生研究院）；Institut Pasteur, Université Paris Cité（巴黎研究院）；University of Wisconsin（威斯康星大学）；University of La Rioja（拉里奥ja大学）；VSB – Technical University of Ostrava（奥斯特拉瓦技术大学）；Masaryk University（马萨里克大学）；MRC Laboratory of Molecular Biology（分子生物学实验室）；University of Cambridge（剑桥大学）；Donostia International Physics Center (DIPC)（Donostia国际物理中心）；CNRS UMR 3691；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France（斯特拉斯堡大学、法国国家科学研究中心、法国国家医学研究院、ICube、UMR7357、法国斯特拉斯堡）；Department of General Surgery, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan（高雄长庚纪念医院外科部、长庚大学医学院）
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；State Key Laboratory of Space-Ground Integrated Information Technology（天地一体化信息技术国家重点实验室）
Deep learning-based interactive segmentation in remote sensing
National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara（加州大学圣塔芭芭拉分校国家生态分析与综合中心）；University of Idaho（爱达荷大学）；Fujian Agriculture and Forestry University（福建农林大学）；Bren School of Environmental Science and Management, University of California, Santa Barbara（加州大学圣塔芭芭拉分校布伦环境科学与管理学院）
