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- Pietro Nardelli (Brigham and Women’s Hospital, Harvard Medical School, USA)
- Jorge Onieva Onieva (Brigham and Women’s Hospital, Harvard Medical School, USA)
- Raúl San José Estépar (Brigham and Women’s Hospital, Harvard Medical School, USA)
The goal of this project is to create a new tool for SlicerCIP that allows the creation of a PDF report to summarize and illustrate quantitive analysis.
SlicerCIP is an extension to Slicer that integrates:
- CIP functionality as a Toolkit exposing of the CLIs.
- Slicer specific modules to provide user-friendly chest CT quantitative solutions.
- Visualization of scale-space particles and labelmaps
- Integrated workflows to end-to-end clinical evaluation
Slicer CIP has been conceived as a workstation for radiologists, but is also suitable for any kind of researchers working on lung, heart or vascular diseases.
- Extend and generalize last year report tool to be used in all SlicerCIP modules.
Approach and Plan
- Implement a general HTML template for quantitative analysis report
- Implement a new module that fills the HTML with images and data
- Add in each SlicerCIP moule a link to the quantitative analysis report
Progress and Next Steps
- General HTML template generated
- Each SlicerCIP module have now a link to create a personalized quantitative analysis report
Background and References
- Source code: https://github.com/acil-bwh/SlicerCIP
- Artery-Vein Classification using Deep-Learning: Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Washko GR, Rahaghi FN, Ledesma-Carbayo MJ, Estépar RS. Pulmonary Artery-Vein Classification in CT Images Using Deep Learning. IEEE Transactions on Medical Imaging.