Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning
This work addresses plaque classification for medical imaging in cardiology, representing an incremental improvement by applying existing deep learning methods to a specific domain task.
The paper tackled automatic plaque classification in coronary arteries from IVOCT images by using convolutional neural networks and transfer learning, achieving results that leverage deep feature learning to avoid handcrafted features and incorporate contextual information between A-Scans.
Advanced atherosclerosis in the coronary arteries is one of the leading causes of deaths worldwide while being preventable and treatable. In order to image atherosclerotic lesions (plaque), intravascular optical coherence tomography (IVOCT) can be used. The technique provides high-resolution images of arterial walls which allows for early plaque detection by experts. Due to the vast amount of IVOCT images acquired in clinical routines, automatic plaque detection has been addressed. For example, attenuation profiles in single A-Scans of IVOCT images are examined to detect plaque. We address automatic plaque classification from entire IVOCT images, the cross-sectional view of the artery, using deep feature learning. In this way, we take context between A-Scans into account and we directly learn relevant features from the image source without the need for handcrafting features.