1. Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets
- Author
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Yazan Gharaibeh, Chaitanya Kolluru, David L. Wilson, Hao Wu, Hiram G. Bezerra, David Prabhu, and Emile Mehanna
- Subjects
Paper ,Conditional random field ,Support Vector Machine ,Databases, Factual ,Computer science ,Feature extraction ,Biomedical Engineering ,Feature selection ,Coronary Artery Disease ,030204 cardiovascular system & hematology ,01 natural sciences ,Imaging ,Machine Learning ,010309 optics ,Biomaterials ,03 medical and health sciences ,0302 clinical medicine ,cryo-imaging ,Optical coherence tomography ,Image Interpretation, Computer-Assisted ,0103 physical sciences ,medicine ,Humans ,Segmentation ,medicine.diagnostic_test ,Contextual image classification ,business.industry ,Endovascular Procedures ,Pattern recognition ,Image segmentation ,Coronary Vessels ,intravascular optical coherence tomography ,Plaque, Atherosclerotic ,Atomic and Molecular Physics, and Optics ,3. Good health ,Electronic, Optical and Magnetic Materials ,Support vector machine ,Artificial intelligence ,business ,Algorithms ,Tomography, Optical Coherence - Abstract
We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous studies (e.g., optical attenuation and A-line peaks). In addition, we included vascular lumen morphology and three-dimensional (3-D) digital edge and texture features. Classification methods were developed using expansive datasets (∼7000 images), consisting of both clinical in-vivo images and an ex-vivo dataset, which was validated using 3-D cryo-imaging/histology. Conditional random field was used to perform 3-D classification noise cleaning of classification results. We tested various multiclass approaches, classifiers, and feature selection schemes and found that a three-class support vector machine with minimal-redundancy-maximal-relevance feature selection gave the best performance. We found that inclusion of our morphological and 3-D features improved overall classification accuracy. On a held-out test set consisting of >1700 images, we obtained an overall accuracy of 81.58%, with the following (sensitivity/specificity) for each class: other (81.43/89.59), fibrolipidic (94.48/87.32), and fibrocalcific (74.82/95.28). The en-face views of classification results showed that automated classification easily captured the preponderance of a disease segment (e.g., a calcified segment had large regions of fibrocalcific classifications). Finally, we demonstrated proof-of-concept for streamlining A-line classification output with existing fibrolipidic and fibrocalcific boundary segmentation methods, to enable fully automated plaque quantification. The results suggest that our classification approach is a viable step toward fully automated IVOCT plaque classification and segmentation for live-time treatment planning and for offline assessment of drug and biologic therapeutics.
- Published
- 2019