A deep reinforcement learning framework for frame-by-frame plaque tracking on intravascular optical coherence tomography image

Abstract

Intravascular Optical Coherence Tomography (IVOCT) is considered as the gold standard for the atherosclerotic plaque analysis in clinical application. A continuous and accurate plaque tracking algorithm is critical for coronary heart disease diagnosis and treatment. However, continuous and accurate plaque tracking frame-by-frame is very challenging because of some difficulties from IVOCT imaging conditions, such as speckle noise, complex and various intravascular morphology, and large numbers of IVOCT images in a pullback. To address such a challenging problem, for the first time we proposed a novel Reinforcement Learning (RL) based framework for accurate and continuous plaque tracking frame-by-frame on IVOCT images. In this framework, eight transformation actions are well-designed for IVOCT images to fit any possible changes of plaque’s location and scale, and the spatio-temporal location correlation information of adjacent frames is modeled into state representation of RL to achieve continuous and accurate plaque detection, avoiding potential omissions. What’s more, the proposed method has strong expansibility, because the fully-automated and semi-automated tracking patterns are both allowed to fit the clinical practice. Experiments on the large-scale IVOCT data show that the plaque-level accuracy of the proposed method can achieve 0.89 and 0.94 for the fully-automated tracking pattern and semi-automated tracking pattern respectively. This proves that our method has big application potential in future clinical practice. The code is open accessible: https://github.com/luogongning/PlaqueRL.