Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Tianjin, 300350, China.
Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
Lasers Med Sci. 2022 Dec 24;38(1):21. doi: 10.1007/s10103-022-03665-2.
Identification and classification of surrounding neck tissues are very important in thyroid surgery. The advantages of optical coherence tomography (OCT), high resolution, non-invasion, and non-destruction make it have great potential in identifying different neck tissues during thyroidectomy. We studied the automatic classification for neck tissues in OCT images based on convolutional neural network in this paper. OCT images of five kinds of neck tissues were collected firstly by our home-made swept source (SS-OCT) system, and a dataset was built for neural network training. Three image classification neural networks: LeNet, VGGNet, and ResNet, were used to train and test the dataset. The impact of transfer learning on the classification of neck tissue OCT images was also studied. Through the comparison of accuracy, it was found that ResNet has the best classification accuracy among the three networks. In addition, transfer learning did not significantly improve the accuracy, but it can somewhat accelerate the convergence of the network and shorten the network training time.
在甲状腺手术中，识别和分类周围颈部组织非常重要。光学相干断层扫描（OCT）具有高分辨率、非侵入性和非破坏性的优点，使其在甲状腺切除术中识别不同的颈部组织方面具有很大的潜力。本文基于卷积神经网络研究了 OCT 图像中颈部组织的自动分类。首先，我们使用自制的扫频源（SS-OCT）系统采集了五种颈部组织的 OCT 图像，并建立了一个用于神经网络训练的数据集。使用三种图像分类神经网络：LeNet、VGGNet 和 ResNet 对数据集进行训练和测试。还研究了迁移学习对颈部组织 OCT 图像分类的影响。通过对准确性的比较，发现 ResNet 在这三种网络中具有最佳的分类准确性。此外，迁移学习并没有显著提高准确性，但它可以在一定程度上加快网络的收敛速度并缩短网络训练时间。
