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Interactive Segmentation of Lung Tissue and Lung Excursion in Thoracic Dynamic MRI Based on Shape-guided
Medrxiv : the Preprint Server for Health Sciences
|May 15, 2024
Summary
This study introduces an interactive deep-learning system for accurate lung segmentation in dynamic MRI, improving Thoracic Insufficiency Syndrome assessment. The method achieves high accuracy in segmenting lung tissue and excursion, aiding clinical diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Thoracic Surgery
Background:
- Accurate lung segmentation in dynamic MRI is crucial for assessing respiratory disorders like Thoracic Insufficiency Syndrome (TIS).
- Existing automatic segmentation methods struggle with image variability and low contrast, hindering robustness.
Conclusions:
- The proposed deep-learning system offers accurate and efficient lung segmentation in dMRI.
- This approach has significant potential for routine clinical assessment of TIS patients using dMRI.
