By integrating salient visual features from different organs and tissues into structured data, the system assists in automating feature annotation. This accelerates the image data collection process, establishing a strong technical foundation for exploring future image-guided analysis technologies.
Leveraging our intelligent annotation platform, we adapted the ScribbleSup concept to endoscopic imaging to streamline and standardize the labeling process.
With integrated visual markers, the system can reliably distinguish and segment key anatomical structures, including:
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FuDali™
•Capable of testing and integrating with the hospital’s core Picture Archiving and Communication System (PACS) to effectively consolidate endoscopic images and clinical data.
•Provides a specialized research and analysis interface for gynecologic imaging, supporting multi-angle and multi-modal image presentation to consolidate essential data for clinical research.
•Performs automated feature annotation on optical images to assist research teams in efficiently identifying anatomical structures and analyzing imaging characteristics, thereby accelerating data collection and model building for overall algorithm development.
