Miyagawa M, Costa M G F, Gutierrez M A, Costa J P G F, Filho C F F Costa
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:600-603. doi: 10.1109/EMBC.2018.8512299.
Lumen segmentation in Optical Coherence Tomography (OCT) images is a very important step to analyze points of interest that may help on atherosclerosis diagnostic and treatment. Past studies use many different methods to segment the lumen in IVOCT images, like level set, morphological reconstruction, Markov random fields, and Otsu binarization. Despite Convolutional Neural Networks (CNN) have shown promising results in the image processing area, we did not identify, in the literature, works applying CNN in IVOCT images. In this paper, we present the lumen segmentation using CNN. We evaluated three different CNN architectures. The CNNs were evaluated using three versions from the image dataset, differing from each other by image size (768x768 pixels and 192x192 pixels), and by coordinate system representation (Cartesian and polar). The best results, Accuracy, Dice index and Jaccard index of over 99%, 98% and 97%, respectively, were obtained with the smallest size images represented by polar coordinate system.
光学相干断层扫描（OCT）图像中的管腔分割是分析可能有助于动脉粥样硬化诊断和治疗的感兴趣点的非常重要的一步。过去的研究使用许多不同的方法来分割血管内光学相干断层扫描（IVOCT）图像中的管腔，如水平集、形态重建、马尔可夫随机场和大津二值化。尽管卷积神经网络（CNN）在图像处理领域已显示出有前景的结果，但我们在文献中未发现将CNN应用于IVOCT图像的工作。在本文中，我们展示了使用CNN进行管腔分割。我们评估了三种不同的CNN架构。使用图像数据集的三个版本对CNN进行评估，这三个版本在图像大小（768×768像素和192×192像素）以及坐标系表示（笛卡尔坐标系和极坐标系）方面彼此不同。使用极坐标系表示的最小尺寸图像分别获得了超过99%、9%和97%的最佳结果，即准确率、骰子系数和杰卡德指数。
