A Collaborative Digital Pathology System for Multi-Touch Mobile and Desktop Computing Platforms

被引:6
|
作者
Jeong, W. [1 ]
Schneider, J. [2 ]
Hansen, A. [3 ]
Lee, M. [4 ]
Turney, S. G. [5 ]
Faulkner-Jones, B. E. [6 ,7 ]
Hecht, J. L. [6 ,7 ]
Najarian, R. [6 ,7 ]
Yee, E. [6 ,7 ]
Lichtman, J. W. [5 ]
Pfister, H. [3 ]
机构
[1] Ulsan Natl Inst Sci & Technol UNIST, Sch Elect & Comp Engn, Ulsan, South Korea
[2] King Abdullah Univ Sci & Technol KAUST, Geometr Modeling & Sci Visualizat Ctr, Thuwal, Saudi Arabia
[3] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA
[4] Inha Univ, Sch Informat & Commun Engn, Inchon, South Korea
[5] Harvard Univ, Dept Mol & Cellular Biol, Cambridge, MA 02138 USA
[6] Beth Israel Deaconess Med Ctr, Dept Pathol, Boston, MA USA
[7] Harvard Univ, Sch Med, Cambridge, MA 02138 USA
基金
新加坡国家研究基金会;
关键词
biomedical image visualization; collaborative visualization; GPU; image compression; digital pathology;
D O I
10.1111/cgf.12137
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Collaborative slide image viewing systems are becoming increasingly important in pathology applications such as telepathology and E-learning. Despite rapid advances in computing and imaging technology, current digital pathology systems have limited performance with respect to remote viewing of whole slide images on desktop or mobile computing devices. In this paper we present a novel digital pathology client-server system that supports collaborative viewing of multi-plane whole slide images over standard networks using multi-touch-enabled clients. Our system is built upon a standard HTTP web server and a MySQL database to allow multiple clients to exchange image and metadata concurrently. We introduce a domain-specific image-stack compression method that leverages real-time hardware decoding on mobile devices. It adaptively encodes image stacks in a decorrelated colour space to achieve extremely low bitrates (0.8 bpp) with very low loss of image quality. We evaluate the image quality of our compression method and the performance of our system for diagnosis with an in-depth user study.
引用
收藏
页码:227 / 242
页数:16
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