2017 IEEE International Conference on Image Processing (ICIP) 2017
DOI: 10.1109/icip.2017.8296678
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Virtual review of large scale image stack on 3D display

Abstract: Large scale images allow pathologists to perform reviews, using computer workstations instead of microscopes. This trend raises a wide range of issues related to the management of these massive datasets. In particular, efficient solutions for data storage and processing have to be developed in order to deliver increasingly reliable and faster analyses. In addition, the improvement of workflows also requires the reinforcement of visualization capabilities. In this paper, we present a new virtual microscopy (VM)… Show more

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Cited by 2 publications
(3 citation statements)
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“…In order to test our GPU cache system, we developed two kinds of visualization applications and a processing stage. The first is a 2D rendering virtual microscope with a navigation through an image stack [28] that provides easy access to high-resolution images. The second is a GPU-based volume ray casting [5].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In order to test our GPU cache system, we developed two kinds of visualization applications and a processing stage. The first is a 2D rendering virtual microscope with a navigation through an image stack [28] that provides easy access to high-resolution images. The second is a GPU-based volume ray casting [5].…”
Section: Discussionmentioning
confidence: 99%
“…It reaches 3840 × 2160 × 4 × 4 ≈ 133 MB for a 4K viewport with the same configuration. These values may have to be multiplied by N in a context of visualization with N views (stereo or multiscopic) [28]. For instance, a 4K screen with 11 views it represents 133 × 11 ≈ 1.4 GB.…”
Section: Comparison With Other Systemsmentioning
confidence: 99%
“…The proposed system fully runs on GPUs [15] and is composed of the virtual microscope and the out-of-core data management. The whole pipeline is called visualization-driven.…”
Section: System Overviewmentioning
confidence: 99%