Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images (2018)

We develop neural-network-based methods for classifying plaque types in clinical intravascular optical coherence tomography (IVOCT) images of coronary arteries. A single IVOCT pullback can consist of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>></mml:mo> <mml:mn>500</mml:mn></mml:mrow> </mml:math> microscopic-resolution images, creating both a challenge for physician interpretation during an interventional procedure and an opportunity for automated analysis. In the proposed method, we classify each A-line, a datum element that better captures physics and pathophysiology than a voxel, as a fibrous layer followed by calcification (fibrocalcific), a fibrous layer followed by a lipidous deposit (fibrolipidic), or other. For A-line classification, the usefulness of a convolutional neural network (CNN) is compared with that of a fully connected artificial neural network (ANN). A total of 4469 image frames across 48 pullbacks that are manually labeled using consensus labeling from two experts are used for training, evaluation, and testing. A 10-fold cross-validation using held-out pullbacks is applied to assess classifier performance. Noisy A-line classifications are cleaned by applying a conditional random field (CRF) and morphological processing to pullbacks in the <i>en-face</i> view. With CNN (ANN) approaches, we achieve an accuracy of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>77.7</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>4.1</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>79.4</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2.9</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> ) for fibrocalcific, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>86.5</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2.3</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>83.4</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2.6</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> ) for fibrolipidic, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>85.3</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2.5</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>82.4</mml:mn> <mml:mo>%</mml:mo> <mml:mo>±</mml:mo> <mml:mn>2.2</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> ) for other, across all folds following CRF noise cleaning. The results without CRF cleaning are typically reduced by 10% to 15%. The enhanced performance of the CNN was likely due to spatial invariance of the convolution operation over the input A-line. The predicted <i>en-face</i> classification maps of entire pullbacks agree favorably to the annotated counterparts. In some instances, small error regions are actually hard to call when re-examined by human experts. Even in worst-case pullbacks, it can be argued that the results will not negatively impact usage by physicians, as there is a preponderance of correct calls.