Computer Science & Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, 121001 Haryana, India.
Department of IT, G.L Bajaj Institute of Technology & Management, Greater Noida, India.
Biomed Res Int. 2022 Jul 18;2022:9112587. doi: 10.1155/2022/9112587. eCollection 2022.
Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method's effectiveness is verified on a fully annotated dataset.
前列腺癌是全世界男性最常见的癌症之一，仅次于肺癌。诊断前列腺癌最常用的方法是病理学家对染色活检进行微观观察和对组织微阵列图像进行 Gleason 评分。然而，病理学家在许多组织微阵列图像下使用 Gleason 模式对前列腺癌组织微阵列进行评分既耗时，又容易受到不同观察者之间的主观因素的影响，并且重复性低。在这项研究中，我们使用了深度学习和计算机视觉这两种最常见的技术，因为深度学习和计算机视觉的发展使病理学计算机辅助诊断系统更加客观和可重复。此外，我们的研究中使用的 U-Net 网络是医学图像分割中使用最广泛的网络。与之前研究中使用的分类器不同，我们提出了一种基于改进的 U-Net 网络的区域分割模型，该模型通过密集连接块融合了深层和浅层。同时，对每个尺度的特征进行监督。作为研究的结果，网络参数可以减少，计算效率可以提高，并且该方法在完全注释的数据集上验证了有效性。
