SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
Qi Duan, Guotai Wang, Rui Wang, Chao Fu, Xinjun Li, Na Wang, Yechong, Huang, Xiaodi Huang, Tao Song, Liang Zhao, Xinglong Liu, Qing Xia, Zhiqiang, Hu, Yinan Chen, Shaoting Zhang

TL;DR
SenseCare is a versatile research platform that integrates AI tools, advanced 3D visualization, and secure web-based access to facilitate clinical research and diagnosis in medical imaging across various applications.
Contribution
The paper introduces SenseCare, a comprehensive, clinic-oriented platform with AI toolkits and features supporting diverse clinical scenarios and multi-center collaborative research.
Findings
Successful deployment in multiple hospitals.
Supports a wide range of clinical applications.
Provides efficient, secure, and extensible image analysis tools.
Abstract
Clinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios. To enable clinical research with Artificial Intelligence (AI), SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. In addition, SenseCare is clinic-oriented and supports a wide range of clinical applications such as diagnosis and surgical planning for lung cancer, pelvic tumor, coronary artery disease, etc. SenseCare provides several appealing functions and features such…
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Taxonomy
TopicsAI in cancer detection · Medical Image Segmentation Techniques · Radiomics and Machine Learning in Medical Imaging
