Advanced diffusion-weighted magnetic resonance imaging in the evaluation of white matter axons in patients with idiopathic normal pressure hydrocephalus

With the increasing application of regenerative medicine in vivo,non-invasive and accurate methods for estimating the white matter axons in neuronal tissue have become increasingly important.As a non-invasive method for patients,magnetic resonance(MR)imaging has demonstrated potential as a promising tool for evaluating axons in vivo.In particular,diffusion-weighted MR imaging(d MRI)and its applications,such as white matter tractography

Coronary arterial bypass graft patency evaluated by multi-detector computed tomography and magnetic resonance imaging

The progression of atherosclerosis of the coronary artery does not stop after coronary arterial bypass grafting (CABG) surgery.……

PANCREATO-BILIARY TUMOR: MR DIAGNOSIS AND RESECTABILITY ASSESSMENT

Objective: TO evaluate the clinical value of MR multi-imaging technique in diagnosing and assessing the resectability of pancreato-biliary tumor. Methods The prospective diagnosis and resectability of 17 patients with suspicious pancreato-biliary tumors were evaluated. Surgical findings and pathologic results confirmed pancreatic adenocarcinoma in 11 cases, cholangiocarcinoma in 4, and non-neoplastic lesion in 2. MR multi-imaging protocol, including MR cross-sectional imaging, us cholangiopancreatography (MRCP ), and three-dimensional dynamic contrast-enhanced MR portography (3D DCE MRP), were performed in all patients. Results MR multi-imaging technique allowed-correct diagnosing 15 of 17 (88. 2% ) patients with pancreato-biliary tumors. The accuracy in detecting the range of tumor invasion was 64. 4%. The sensitivity, speificity, accuracy, positive, and negative predictive value of MR multi-imaging technique in assessing the resectability of pancreato-biliary tumors were 83. 3%, 77. 8%, 80. 0%, 71. 4%, and 87. 5%, respectively. Conclusion MR multi-imaging technique can not only improve the diagnostic ability of pancreato-biliary tumor, but also assess the surgical reartability of the tumor. With the fast development of MR techniques, the diagnosing and pre-operative assessment of aoncreato-biliary tumor may be more simplified and efficient by using the non-invasive "all-in-one" method.

Image aesthetic quality evaluation using convolution neural network embedded learning

A way of embedded learning convolution neural network(ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

Deep image synthesis from intuitive user input:A review and perspectives

In many applications of computer graphics,art,and design,it is desirable for a user to provide intuitive non-image input,such as text,sketch,stroke,graph,or layout,and have a computer system automatically generate photo-realistic images according to that input.While classically,works that allow such automatic image content generation have followed a framework of image retrieval and composition,recent advances in deep generative models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and flow-based methods have enabled more powerful and versatile image generation approaches.This paper reviews recent works for image synthesis given intuitive user input,covering advances in input versatility,image generation methodology,benchmark datasets,and evaluation metrics.This motivates new perspectives on input representation and interactivity,cross fertilization between major image generation paradigms,and evaluation and comparison of generation methods.

Versus—A tool for evaluating visualizations and image quality using a 2AFC methodology

Novel visualization methods and strategies are necessary to cope with the deluge of datasets present in any scientific field to make discoveries and find answers to previously unanswered questions.These methods and strategies should not only present scientific findings as images in a concise way but also need to be effective and expressive,which often remain untested.Here,we present Versus,a tool to enable easy image quality assessment and image ranking,utilizing a two-alternative forced choice methodology(2AFC)and an efficient ranking algorithm based on a binary search.The tool provides a systematic way of setting up evaluation experiments via the web without the necessity to install any additional software or require any programming skills.Furthermore,Versus can easily interface with crowdsourcing platforms,such as Amazon’s Mechanical Turk,or can be used as a stand-alone system to carry out evaluations with experts.We demonstrate the use of Versus by means of an image evaluation study,aiming to determine if hue,saturation,brightness,and texture are good indicators of uncertainty in three-dimensional protein structures.Drawing from the power of crowdsourcing,we argue that there is demand and also great potential for this tool to become a standard for simple and fast image evaluations,with the aim to test the effectiveness and expressiveness of scientific visualizations.