Chen Jun, Wan Zhechao, Zhang Jiacheng, Li Wenhua, Chen Yanbing, Li Yuebing, Duan Yue
Department of Urology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, No.318 Chaowang Road, Gongshu District, Hangzhou 310005 China.
Department of Urology, Zhuji Central Hospital, No.98 Zhugong Road, Jiyang Street, Zhuji City, 311800, Zhejiang Province, China.
Comput Methods Programs Biomed. 2021 Mar;200:105878. doi: 10.1016/j.cmpb.2020.105878. Epub 2020 Nov 27.
Prostate cancer is a disease with a high incidence of tumors in men. Due to the long incubation time and insidious condition, early diagnosis is difficult; especially imaging diagnosis is more difficult. In actual clinical practice, the method of manual segmentation by medical experts is mainly used, which is time-consuming and labor-intensive and relies heavily on the experience and ability of medical experts. The rapid, accurate and repeatable segmentation of the prostate area is still a challenging problem. It is important to explore the automated segmentation of prostate images based on the 3D AlexNet network.
Taking the medical image of prostate cancer as the entry point, the three-dimensional data is introduced into the deep learning convolutional neural network. This paper proposes a 3D AlexNet method for the automatic segmentation of prostate cancer magnetic resonance images, and the general network ResNet 50, Inception -V4 compares network performance.
Based on the training samples of magnetic resonance images of 500 prostate cancer patients, a set of 3D AlexNet with simple structure and excellent performance was established through adaptive improvement on the basis of classic AlexNet. The accuracy rate was as high as 0.921, the specificity was 0.896, and the sensitivity It is 0.902 and the area under the receiver operating characteristic curve (AUC) is 0.964. The Mean Absolute Distance (MAD) between the segmentation result and the medical expert's gold standard is 0.356 mm, and the Hausdorff distance (HD) is 1.024 mm, the Dice similarity coefficient is 0.9768.
The improved 3D AlexNet can automatically complete the structured segmentation of prostate magnetic resonance images. Compared with traditional segmentation methods and depth segmentation methods, the performance of the 3D AlexNet network is superior in terms of training time and parameter amount, or network performance evaluation. Compared with the algorithm, it proves the effectiveness of this method.
前列腺癌是男性中肿瘤发病率较高的疾病。由于潜伏期长且病情隐匿，早期诊断困难；尤其是影像学诊断更为困难。在实际临床实践中，主要采用医学专家手动分割的方法，该方法耗时费力，且严重依赖医学专家的经验和能力。前列腺区域的快速、准确且可重复分割仍然是一个具有挑战性的问题。探索基于3D AlexNet网络的前列腺图像自动分割具有重要意义。
以前列腺癌医学图像为切入点，将三维数据引入深度学习卷积神经网络。本文提出一种用于前列腺癌磁共振图像自动分割的3D AlexNet方法，并与通用网络ResNet 50、Inception -V4比较网络性能。
基于500例前列腺癌患者的磁共振图像训练样本，在经典AlexNet的基础上通过自适应改进建立了一组结构简单且性能优异的3D AlexNet。准确率高达0.921，特异性为0.896，敏感性为0.902，受试者操作特征曲线下面积（AUC）为0.964。分割结果与医学专家金标准之间的平均绝对距离（MAD）为0.356毫米，豪斯多夫距离（HD）为1.024毫米，骰子相似系数为0.9768。
改进后的3D AlexNet能够自动完成前列腺磁共振图像的结构化分割。与传统分割方法和深度分割方法相比，3D AlexNet网络在训练时间、参数量或网络性能评估方面表现更优。与算法相比，证明了该方法的有效性。
