In this white paper, see an example of using an imaging data management platform to conduct a comprehensive comparison of two deep learning-based CT segmentation algorithms for liver and kidney segmentation—Total-Segmentator and Swin-UNETR. We evaluate their performance on an independent, open-source CT dataset, to address the challenges of segmentation performance across different datasets. The comparison reveals the importance of complete DICOM header curation to identify conditions that contribute to segmentation errors.
Back to Insights
White Papers
