Coronary Artery Segmentation Based on Transfer Learning and UNet Architecture on Computed Tomography Coronary Angiography Images
Belén Serrano-Antón, Alberto Otero-Cacho, Diego López‐Otero, Brais Díaz‐Fernández, María Bastos-Fernández, V. Pérez‐Muñuzuri, José Ramón González‐Juanatey, Alberto P. Muñuzuri
Abstract
Coronary artery segmentation from CT scans is a helpful tool for coronary artery diseases diagnosis, which is frequently characterised by a vessel narrowing (stenosis). This is a highly demanded and high time-consuming process, thus automated procedures are becoming increasingly necessary. In this work, we propose an extremely light computationally 2D UNet that uses transfer learning for the first time in CT images. We compare the results, using different architectures and backbones, of a 2D UNet and a 3D UNet trained from scratch (i.e. weights are randomly initialised) and a 2D EfficientUNet. Both the amount of input data, with a total of 88 patients, and the extension of the structure to be recognised, the aorta and the coronary arteries ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C.A</i> ), as well as the coronary arteries only ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C.A</i> ) are analysed. Network outputs in clinically identified stenotic lesion areas are also assessed. The results show the advantage of using transfer learning when data is scarce, improving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score by up to 0.6 points for the 2D UNet. On the other hand, when data is sufficient, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score values are close to 0.9 for all the networks. Besides, the results reveal that the 2D UNet distinguishes the thinnest and most distal vessels, although in the presence of a lesion, there is a clear tendency to overestimate it. The network with the best accuracy is the 3D UNet, with values above 95% and 75% in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C.A</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C.A</i> , respectively. Moreover, the proposed methods show dependence on the amount of training data and dataset structure ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C.A</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C.A</i> ).