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Collaborative SDOCT segmentation and analysis software

- Yun, Yeyi, Carass, Aaron, Lang, Andrew, Prince, Jerry, Antony, Bhavna

  • Authors: Yun, Yeyi , Carass, Aaron , Lang, Andrew , Prince, Jerry , Antony, Bhavna
  • Type: Text , Conference proceedings
  • Relation: SPIE Medical Imaging:Imaging Informatics for Healthcare, Research. and Applications, 2017; Florida, United States Vol. 10138
  • Full Text: false
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  • Description: Spectral domain optical coherence tomography (SDOCT) is routinely used in the management and diagnosis of a variety of ocular diseases. This imaging modality also finds widespread use in research, where quantitative measurements obtained from the images are used to track disease progression. In recent years, the number of available scanners and imaging protocols grown and there is a distinct absence of a unified tool that is capable of visualizing, segmenting, and analyzing the data. This is especially noteworthy in longitudinal studies, where data from older scanners and/or protocols may need to be analyzed. Here, we present a graphical user interface (GUI) that allows users to visualize and analyze SDOCT images obtained from two commonly used scanners. The retinal surfaces in the scans can be segmented using a previously described method, and the retinal layer thicknesses can be compared to a normative database. If necessary, the segmented surfaces can also be corrected and the changes applied. The interface also allows users to import and export retinal layer thickness data to an SQL database, thereby allowing for the collation of data from a number of collaborating sites.

Team recognition in big scholarly data: exploring collaboration intensity

- Yu, Shuo, Xia, Feng, Zhang, Kaiyuan, Ning, Zhaolong, Zhong, Jiaofei, Liu, Chengfei

  • Authors: Yu, Shuo , Xia, Feng , Zhang, Kaiyuan , Ning, Zhaolong , Zhong, Jiaofei , Liu, Chengfei
  • Type: Text , Conference proceedings
  • Relation: p. 925-932
  • Full Text: false
  • Reviewed:
  • Description: The scale of scholarly data has been expanded due to the fact that scientific productions are increasing rapidly and new scholars affiliate academia incessantly. Scholars are shifting their research patterns from individual research to academic teamwork due to the complexity of scientific issues. In order to achieve higher reputations and better performance, academic teams with leaders are constructed to speed up knowledge sharing and problem solving. It is significant to explore team-based issues with the increasing interests of information exploration in big scholarly data. However, existing academic team definitions are commonly not quantitative, which makes it difficult to identify real academic teams. In this work, we propose a collaboration relationship evaluation index called Collaboration Intensity Index (CII), which is a two-way and quantitative index to evaluate collaboration intensity between two scholars in the network. Then, we construct a new type of co-author network with edges weighted by CII, which differs from the original co-author networks. This network reflects the newly scientific research patterns inside or outside academic teams. Furthermore, we propose TRAC (Team Recognition Algorithm based on CII) to identify academic teams from large co-author networks. Finally, we use DBLP data set, which contains 1,250,440 scholars and 1,575,949 published papers, to identify teams by TRAC. Comparing with fast unfolding algorithm and real team data, the effectiveness of our method can be demonstrated.