English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cloud based medical image analysis has become popular recently due to thehigh computation complexities of various deep neural network (DNN) basedframeworks and the increasingly large volume of medical images that need to beprocessed. It has been demonstrated that for medical images the transmissionfrom local to clouds is much more expensive than the computation in the cloudsitself. Towards this, 3D image compression techniques have been widely appliedto reduce the data traffic. However, most of the existing image compressiontechniques are developed around human vision, i.e., they are designed tominimize distortions that can be perceived by human eyes. In this paper we willuse deep learning based medical image segmentation as a vehicle and demonstratethat interestingly, machine and human view the compression quality differently.Medical images compressed with good quality w.r.t. human vision may result ininferior segmentation accuracy. We then design a machine vision oriented 3Dimage compression framework tailored for segmentation using DNNs. Our methodautomatically extracts and retains image features that are most important tothe segmentation. Comprehensive experiments on widely adopted segmentationframeworks with HVSMR 2016 challenge dataset show that our method can achievesignificantly higher segmentation accuracy at the same compression rate, ormuch better compression rate under the same segmentation accuracy, whencompared with the existing JPEG 2000 method. To the best of the authors'knowledge, this is the first machine vision guided medical image compressionframework for segmentation in the clouds.

Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds