Authors
L Xie, G J Grevera, Y Tong, C Wu, Z Liu, D A Torigian, J K Udupa
Published in
Proceedings of SPIE--the International Society for Optical Engineering. Volume 13927. Epub Apr 01, 2026.
Abstract
Segmentation in medical images plays a crucial first step for the quantitative analysis of organs of interest and disease conditions that may affect them. In the 2000s, we released an open-source medical image processing and analysis software system called CAVASS (Computer-Assisted Visualization and Analysis Software System), whose lineage goes back to 1980 as the first software for medical image processing. CAVASS supports multiple types of medical images and numerous usage scenarios/use cases. To allow ready access to advanced deep learning models within the easy-to-use graphical user interface (GUI) of CAVASS to greatly facilitate ground truth segmentation generation and production mode image segmentation, we created an enhanced version of CAVASS, called CAVASS-DL, by integrating the Segment Anything Model (SAM) within our software, which can extract and fuse the features from images and interactive prompts to enhance the segmentation performance. Firstly, we modified the GUI to enable intuitive region-of-interest (ROI) prompting via bounding boxes. Secondly, we implemented and trained the SAM network using PyTorch platform. Lastly, we bundled the SAM network as a standalone executable interface for CAVASS. The illustrative and quantitative results on 20 dynamic MRI scans of neck and thorax demonstrate that our approach achieves high accuracy and efficiency for semi-automatic anatomy object segmentation via CAVASS-DL in a convenient manner.
PMID:
42894741
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.
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