Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball movementscan cause misalignment between consecutive images. The multispectral imagesequence reveals important information in the form of retinal and choroidal bloodvessel maps, which can help ophthalmologists to analyze the morphology of theseblood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deeplearning framework called “Adversarial Segmentation and Registration Nets”(ASRNet) for the simultaneous estimation of the blood vessel segmentation andthe registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills theblood vessel segmentation task, and (ii) A registration module R that estimatesthe spatial correspondence of an image pair. Based on the segmention-drivenregistration network, we train the segmentation network using a semi-supervisedadversarial learning strategy. Our experimental results show that the proposedASRNet can achieve state-of-the-art accuracy in segmentation and registrationtasks performed with real MSI datasets.
为了对药用植物进行信息化管理,设计一个基于web的药用植物信息管理系统,对药用植物进行细化分类,让用户能够通过系统获取药用植物百科信息,对药用植物进行更好的探索和研究。基于web的药用植物信息管理系统采用B/S架构,Microsoft Visual Studio 2008开发环境和ASP.NET,以及SQL Server 2008数据库。
本设计基于B/S模式,运用ASP.NET技术,采用功能强大的Microsoft Visual Studio 2008作为开发工具、Sql Server作为数据库而开发出来的综合测评管理系统。开发本系统可减轻教务工作压力,比较系统地对教务、教学上的各项服务和信息进行管理。同时,可以减少劳动力的使用,加快查询速度、加强管理,使各项管理更加规范化。
