Deep HT: A deep neural network for diagnose on MR images of tumors of the hand
Deep HT: A deep neural network for diagnose on MR images of tumors of the hand
复制标题
Deep HT：用于诊断手部肿瘤 MR 图像的深度神经网络
DOI：
10.1371/journal.pone.0237606
复制
发表时间：
2020-08-14
期刊：
影响因子：
3.7
通讯作者：
Lu, Hui
中科院分区：
文献类型：
--
作者：
Hu, Xianliang;Liu, Zongyu;Lu, Hui
手部肿瘤种类繁多，影像学诊断医师往往难以做出正确诊断，容易导致误诊和延误治疗。因此，在本文中，我们提出了一种用于手部肿瘤MR图像诊断的深度神经网络，以便更好地定义术前诊断和规范手术治疗。方法收集某医疗中心2016 - 2019年221例手部肿瘤患者的MRI图像，邀请医学专家对图像进行标注，形成标注数据集。然后对原始图像进行预处理，得到图像数据集。数据集随机分为十个部分，九个用于训练，一个用于测试。接下来，将数据集输入神经网络系统进行测试。最后，对十个实验的结果进行平均，作为对算法精度的估计。结果本研究使用221张图像作为数据集，系统对手部肿瘤的分割平均置信度为71.6%。分割的肿瘤区域通过基础真值分析和放射科医生的人工分析进行验证。随着卷积神经网络的最新进展，图像分割已经取得了巨大的进步，主要是基于跳过连接的编码器解码器深度架构。因此，本文提出了一种基于DeepLab v3+的自动分割方法，并取得了较好的诊断准确率。
Background There are many types of hand tumors, and it is often difficult for imaging diagnosticians to make a correct diagnosis, which can easily lead to misdiagnosis and delay in treatment. Thus in this paper, we propose a deep neural network for diagnose on MR Images of tumors of the hand in order to better define preoperative diagnosis and standardize surgical treatment. Methods We collected MRI figures of 221 patients with hand tumors from one medical center from 2016 to 2019, invited medical experts to annotate the images to form the annotation data set. Then the original image is preprocessed to get the image data set. The data set is randomly divided into ten parts, nine for training and one for test. Next, the data set is input into the neural network system for testing. Finally, average the results of ten experiments as an estimate of the accuracy of the algorithm. Results This research uses 221 images as dataset and the system shows an average confidence level of 71.6% in segmentation of hand tumors. The segmented tumor regions are validated through ground truth analysis and manual analysis by a radiologist. Conclusions With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder decoder deep architectures. Therefore, in this paper, we propose an automatic segmentation method based on DeepLab v3+ and achieved a good diagnostic accuracy rate.