Project: New Slicer Module for Visual Assessment of Pulmonary Congestion from Ultrasound

[goldenfire6]

Draft Status

Draft - team will hold off on page creation

Category

Segmentation / Classification / Landmarking

Key Investigators

• Mike Jin (Centaur Labs, Brigham and Women's Hospital, USA)
• Tamas Ungi (Queens University, Canada)
• Fahimeh Fooladgar (University of British Columbia, Canada)
• Tina Kapur (Brigham and Women's Hospital, USA)

Project Description

This work is part of an NIH Trailblazer R21 grant to our team to develop and validate computational methods for quantifying pulmonary congestion using B-lines in heart failure patients from bedside lung ultrasound in emergency settings. Tools for automated quantification could help emergency department physicians more rapidly and frequently examine patients to assess progress and adjust treatment, resulting in improved care and patient outcomes.

Objective

1. Add a new public module for annotation of pulmonary congestion in ultrasound
2. Add new feature to existing public Anonymizer module: AI-assisted detection of image fan boundaries in ultrasound to streamline anonymization, followed by OCR in output which produces warning if any text is detected in image

Approach and Plan

We will spend Project Week developing the software to support these features and hopefully release the modules publicly.

Progress and Next Steps

No response

Illustrations

No response

Background and References

1. Asgari-Targhi et al. (2024). Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound? MICCAI 2024. (link to paper)

[sjh26]

@goldenfire6 is this ready to submit?

[github-actions]

Project Page Pull Request Creation

✅ COMPLETED: See #1404