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## Automated CT diaphragm muscle segementation

## Key Investigators
- Adamo Donovan (McGill University)
- Dr. Benjamin McDonald Smith (McGill University)
- Investigator 3 (Affiliation)

# Project Description
Manual diaphragm segmentation requires 2 hours per left hemi-diaphragm for a trained rater.

Previous automated methods of CT diaphragm segmentation, have relied on using a priori anatomical
knowledge (i.e. the lower surface of the lungs as a diaphragmatic landmark), mathematical models, and other
assumptions in order to segment the diaphragm, which was then compared to the gold standard manual
segmentation performed by expert radiologists. This method outputed measures of diaphragm distances,
surface area, and curvature.

Our method would be the first to use the gold standard method of over 300 manual and direct diaphragmatic
segmentations to train an artificial intelligence and neural network that would  create a three-dimensional model
of the left hemi-diaphragm with measures of density, volume, and dome height.

## Objective
1. Objective A. To automate diaphragm muscle segmentation from chest CT images using 300 manually segmented diaphragms
along with an artificial neural network approach.

## Approach and Plan

1. Describe planned approach to reach objectives.
1. ...
1. ...

## Progress and Next Steps

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# Illustrations

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# Background and References

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- Source code: https://github.com/YourUser/YourRepository
- Documentation: https://link.to.docs
- Test data: https://link.to.test.data