In many medical scenarios, physicians need to annotate pixelwise objects in CT images, whole slide images (WSI), or cellular images. This annotation process often requires a significant amount of time and effort, especially when dealing with large datasets. To address this challenge, a web-based tool capable of automatically segmenting 3D and 2D medical images are widely expected.
EXACT is an existing web-based annotation platform and has already certain user base. Exact supports interdisciplinary collaboration and allows for both online and offline annotation and analysis of images across various domains. Physicians can annotate images directly through the platform’s web interface, which is intuitive and efficient. [1]
To enhance the functionality of Exact, an automatic segmentation plugin is explored and implemented in this thesis and integrate it with Exact. This plugin will enable physicians and researchers to automatically generate high-quality segmentation masks while annotating and save these masks for future use. This approach can significantly improve the efficiency of medical image annotation, reduce manual effort, and optimize medical imaging workflows.
A critical aspect of this project is selecting a segmentation model that is both efficient and accurate. I plan to adopt Segment Anything Model 2 (SAM2), as it has demonstrated robust performance in handling diverse medical imaging tasks (including CT, WSI, and cellular images) while ensuring segmentation precision and reliability. [2]
[1] Christian Marzahl, Marc Aubreville, Christof A. Bertram, Jennifer Maier, Christian Bergler, Christine Kröger, Jörn Voigt, Katharina Breininger, Robert Klopfleisch, and Andreas Maier. Exact: a collaboration toolset for algorithm-aided annotation of images with annotation version control. Scientific Reports, 11(1):4343, Feb 2021.
[2] Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Doll´ar, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714, 2024.