To overcome these shortcomings, researchers at Arizona State University have developed a machine learning-based approach for automatically detecting the pulmonary trunk. By using a cascaded Adaptive Boosting machine learning algorithm with a large number of digital image object recognition features, this method automatically identifies the pulmonary trunk by sequentially scanning the CTPA images and classifying each encountered sub-image with the trained classifier.
This approach outperforms existing anatomy-based approaches. It requires no explicit representation of anatomical knowledge and achieves a nearly 100% accuracy as tested on a large number of cases.
- Diagnosis of pulmonary embolism
- Discrimination of pulmonary embolism from other hyperbaric injuries
Benefits and Advantages
- Outperforms existing anatomy-based approaches
- Dynamically adapts to suboptimal image contrast
- Discriminates artifacts that may obscure or mimic embolisms
- Capable of detecting central pulmonary embolisms
- Distinguishes the pulmonary artery from the vein to remove false positives
- Requires no explicit representation of anatomical knowledge
- Achieves nearly 100% accuracy as tested on a large number of cases
For more information about the inventor(s) and their research, please see
Dr. Liang's departmental webpage