基于卷积神经网络的CT弦图学习与身体部位识别

Bodypart recognition with CT sinogram based on convolutional neural network

基于医学影像的身体部位识别旨在确定特定医学影像所属的身体部位,是许多医学影像分析任务必不可少的预处理步骤。目前,计算机断层扫描(CT,Computed Tomography)技术是临床中最常用的医学影像技术之一,而基于CT图像的医学影像分析算法(例如,病灶检测、器官分割等)同样需要首先确定CT图像所属身体部位以获取先验知识,从而保证算法的速度及鲁棒性。然而CT图像是由被称为CT弦图(CT Sinogram)的CT原始数据重建得到,而图像重建过程有可能导致信息丢失。因此,相较于CT图像,CT弦图中应该包含更多适用于身体部位识别的有效信息。然而,目前基于CT弦图的身体部位识别研究仍比较少。鉴于此,本研究使用基于卷积神经网络(CNN,Convolutional Neural Network)的深度学习对CT弦图进行特征学习,并验证其在身体部位识别任务中的可用性。实验采用了一个公开数据集(DeepLesion)和来自著名医学机构的三个临床数据集来验证本研究提出方法的性能。具体而言,本研究通过Radon变换理论对CT图像进行数据仿真得到CT弦图,并以CT弦图作为输入,构造基于CNN的分类器(Sino-Net),从而对五个最常见的身体部位(头部、颈部、胸部、上腹部以及骨盆)进行识别。实验结果表明,使用CT弦图进行身体部位识别可以达到与使用CT图像进行身体部位识别相似的性能,甚至优于基于CT图像识别的结果。

Medical-image-based human bodypart recognition,which aims to accurately locate the bodypart of a specific medical image,is an essential preprocessing step for many medical image analysis tasks.Currently,computed tomography(CT)is one of the most available medical imaging techniques in clinic.Many CT-based medical image analysis algorithms(such as lesion detection,organ segmentation,etc.)need to first identify the bodypart information contained in the CT image to obtain prior knowledge,so as to ensure the speed and robustness of the algorithms. However,CT images are reconstructed from CT raw data,which is also known as CT sinogram. And the image reconstruction process may cause information loss. Therefore,compared with CT images,CT sinogram may contain more effective information suitable for bodypart recognition tasks. However,there are still relatively few researches on bodypart recognition based on CT sinogram. Therefore,the deep learning based convolutional neural network(CNN)technique is used to train on CT sinogram and its usability in bodypart recognition tasks is verified. A public dataset(i. e.,DeepLesion)and three clinical datasets from well-known medical institutions are adopted to verify the performance of our proposed method. Specifically,the Radon transform is used to perform data simulation on CT images to obtain CT sinogram,which is served as input to train a CNN-based classifier(Sino-Net)to recognize the five most common bodyparts(i. e.,head,neck,chest,upper abdomen and pelvis). The experimental results show that the use of CT sinogram for bodypart recognition can achieve similar performance to the use of CT images,and sometimes even better than the results based on CT images.