A collaborative digital pathology system for multi-touch mobile and desktop computing platforms

  • W. Jeong
  • , J. Schneider
  • , A. Hansen
  • , M. Lee
  • , S. G. Turney
  • , B. E. Faulkner-Jones
  • , J. L. Hecht
  • , R. Najarian
  • , E. Yee
  • , J. W. Lichtman
  • , H. Pfister

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

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. 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 systems 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.

Original languageEnglish
Pages (from-to)227-242
Number of pages16
JournalComputer Graphics Forum
Volume32
Issue number6
DOIs
Publication statusPublished - Sept 2013
Externally publishedYes

Keywords

  • GPU
  • biomedical image visualization
  • collaborative visualization
  • digital pathology
  • image compression

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