Jun Chen1, Zhechao Wan2, Jiacheng Zhang3, Wenhua Li4, Yanbing Chen5, Yuebing Li6, Yue Duan7. 1. Department of Urology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, No.318 Chaowang Road, Gongshu District, Hangzhou 310005 China. 2. Department of Urology, Zhuji Central Hospital, No.98 Zhugong Road, Jiyang Street, Zhuji City, 311800, Zhejiang Province, China. 3. The 2nd Clinical Medical College, Zhejiang Chinese Medical University, 548 Bin Wen Road, Hangzhou 310053, China. 4. Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine, 1665 Kong Jiang Road, Shanghai 200092, China. 5. Computer Application Technology, School of Applied Sciences, Macao Polytechnic Institute, Macao SAR 999078, China. 6. Department of Anaesthesiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, No.318 Chaowang Road, Gongshu District, Hangzhou 310005 China. Electronic address: lyb1853@zcmu.edu.cn. 7. Department of Urology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, No.318 Chaowang Road, Gongshu District, Hangzhou 310005 China. Electronic address: 20164919@zcmu.edu.cn.
Abstract
BACKGROUND: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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.
BACKGROUND: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. METHOD: 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. RESULTS: Based on the training samples of magnetic resonance images of 500 prostate cancerpatients, 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. CONCLUSION: 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.
Authors: Jianxiu Cai; Manting Liu; Qi Zhang; Ziqi Shao; Jingwen Zhou; Yongjian Guo; Juan Liu; Xiaobin Wang; Bob Zhang; Xi Li Journal: Biomed Res Int Date: 2022-03-28 Impact factor: 3.411