肺结节图像的自动分割与识别

Automated segmentation and identification of pulmonary nodule images

为实现肺结节自动分析与识别,研究基于模糊建模思想和迭代相对模糊连接度(IRFC)算法的自动解剖识别(AAR)方法。该方法包括5个步骤:收集图像数据,用于模型构建和测试AAR;叙述胸腔中每个器官的精确定义,根据定义提取肺部轮廓;建立分层模糊解剖模型;利用分层模型识别和定位肺部;根据层级结构提取肺部轮廓。将分割好的肺部图片作为输入送入卷积神经网络进行肺部结节检测,通过使用VGG-16网络模型,在天池医疗AI大赛的数据集上实现了92.72%的目标检测准确率。

To realize automatic analysis and recognition of pulmonary nodules,an automatic anatomy recognition(AAR)metho-dology based on fuzzy modeling ideas and an iterative relative fuzzy connectedness(IRFC)delineation algorithm was studied.The methodology consisted of five main steps including gathering image date for both building models and testing the AAR algorithms,formulating precise definitions of each organ in the thorax and delineating lungs following these definitions,building hie-rarchical fuzzy anatomy models,recognizing and locating lungs with the hierarchical models,and delineating the lungs following the hierarchy.The segmented lung images were taken as input into the convolutional neural network for pulmonary nodule detection.By the use of the VGG-16 network model,a target detection accuracy of 92.72%is achieved on the data set of the Tianchi Medical AI Contest.