基于深度学习的蝶鞍自动分割研究  

Sella turcica automatic segmentation based on deep learning

作　　者：冯琦 刘曙 颜颋 冯红超 Feng Qi;Liu Shu;Yan Ting;Feng Hongchao(College of Medicine,Guizhou University,Guiyang,Guizhou 550025,China;Guiyang Hospital of Stomatology)

机构地区：[1]贵州大学医学院,贵州贵阳550025 [2]贵阳市口腔医院

出　　处：《计算机时代》2023年第6期134-137,141,共5页Computer Era

基　　金：观山湖区科技计划项目“人工智能技术在口腔颌面医学影像中的应用研究”(观科合同[2021]01号)。

摘　　要：研究蝶鞍的形态变化及生长规律对于口腔医生获取额外诊断信息具有重要意义。本文开发并评估了一个基于深度学习的蝶鞍自动分割模型。实验将包含400张头颅侧位片的数据集随机划分成两个子集,其中360张图像作为训练集来训练蝶鞍的自动分割网络U-net,40张图像作为测试集来测试模型的分割性能。Dice系数用于评估模型的分割性能,训练好的蝶鞍自动分割网络的Dice系数为0.9074。所提方法能够快速准确地在头颅侧位片上自动分割出蝶鞍区域。It is significance to study the morphological changes and growth rules of sella turcica for stomatologist to obtain additional diagnostic information.This paper aims to develop and evaluate a deep learning-based model for automatic segmentation of sella turcica.The experimental dataset containing 400 cephalometric radiographs is randomly divided into two subsets,in which 360 images are used as training dataset to train sella turcica automatic segmentation network,and 40 images are used as test dataset to test the segmentation performance of the model.Dice coefficient is used to evaluate the segmentation performance of the model,and the Dice coefficient of the trained sella turcica automatic segmentation network is 0.9074.The proposed method can quickly and accurately segment the sella turcica on cephalometric radiographs.

关 键 词：蝶鞍 头颅侧位片 深度学习 图像分割 U-net 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]