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首页> 外文会议>International Conference on Medical Image Computing and Computer-Assisted Intervention >A Deep Reinforcement Learning Framework for Frame-by-Frame Plaque Tracking on Intravascular Optical Coherence Tomography Image
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A Deep Reinforcement Learning Framework for Frame-by-Frame Plaque Tracking on Intravascular Optical Coherence Tomography Image

机译：横轴夹斑跟踪对血管内光学相干断层扫描图像的帧间斑块追踪的深度加强学习框架

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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.
机译：血管内光学相干断层扫描（IVOCT）被认为是临床应用中动脉粥样硬化斑块分析的金标准。连续和准确的牙菌斑跟踪算法对于冠心病诊断和治疗至关重要。然而，由于IVOCT成像条件，例如散斑噪声，复杂和各种血管内的形貌，以及回调中的大量IVOCT图像，连续和准确的斑块逐帧逐帧非常具有挑战性。为了解决如此挑战性问题，我们首次提出了一种基于新的加强学习（RL）基于IVOCT图像上的准确和连续斑块跟踪帧框架的框架。在该框架中，八个转换动作是为IVOCT图像设计的，以适应斑块的位置和刻度的任何可能变化，并且相邻帧的时空位置相关信息被建模到RL的状态表示，以实现连续和准确的斑块检测，避免潜在的遗漏。更重要的是，所提出的方法具有很强的可扩展性，因为允许全自动和半自动化的跟踪模式均允许拟合临床实践。大型IVOCT数据的实验表明，该方法的斑块级精度分别可以分别实现0.89和0.94的全自动跟踪模式和半自动跟踪图案。这证明了我们的方法在未来的临床实践中具有大的应用潜力。代码可开放可访问：https://github.com/luogongning/ plaquerl。

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