Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning
| Autor: | Saikiran Rapaka, Yefeng Zheng, Gareth Funka-Lea, Mehmet Akif Gulsun, Puneet Sharma |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science 02 engineering and technology Convolutional neural network 030218 nuclear medicine & medical imaging Coronary arteries 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Orientation tensor Region of interest Robustness (computer science) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Computer vision Tensor Artificial intelligence business Algorithm Computed tomography angiography |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319467252 MICCAI (3) |
| Popis: | We present a novel method for the automated extraction of blood vessel centerlines. There are two major contributions. First, in order to avoid the shortcuts to which minimal path methods are prone, we find optimal paths in a computed flow field. We solve for a steady state porous media flow inside a region of interest and trace centerlines as maximum flow paths. We explain how to estimate anisotropic orientation tensors which are used as permeability tensors in our flow field computation. Second, we introduce a convolutional neural network (CNN) classifier for removing extraneous paths in the detected centerlines. We apply our method to the extraction of coronary artery centerlines found in Computed Tomography Angiography (CTA). The robustness and stability of our method are enhanced by using a model-based detection of coronary specific territories and main branches to constrain the search space [15]. Validation against 20 comprehensively annotated datasets had a sensitivity and specificity at or above 90 %. Validation against 106 clinically annotated coronary arteries showed a sensitivity above 97 %. |
| Databáze: | OpenAIRE |
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