Abstract
Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT).Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation.Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis.Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.
Highlights
Coronary artery disease is the leading cause of death worldwide, accounting for the majority of acute coronary syndromes and sudden cardiac deaths
We evaluated the segmentation performance using the intersection over union (IOU, called Jaccard index), a common metric for evaluation of the quality of segmentation algorithms [28]
In the histopathology data set, a total of 62 lesions from 7 patients were identified according to the classification by Virmani et al [20] and manually co-registered with optical coherence tomography (OCT) as previously described [21]
Summary
Coronary artery disease is the leading cause of death worldwide, accounting for the majority of acute coronary syndromes and sudden cardiac deaths. Invasive assessment of the coronary arteries using high-resolution intravascular imaging has emerged as an important tool for identifying atherosclerotic lesions [4], as the close proximity of the imaging catheter allows a more precise and high-resolution visualization of the vascular tissue compared to non-invasive modalities [5]. We developed DeepAD, a deep-learning algorithm trained on data with histopathologybased annotations from autopsy specimens as well as clinical OCTs for prediction of atherosclerotic lesions, and evaluated it in dedicated real-world cases (Figure 1). Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT)
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