Cloud based medical image analysis has become popular recently due to the
high computation complexities of various deep neural network (DNN) based
frameworks and the increasingly large volume of medical images that need to be
processed. It has been demonstrated that for medical images the transmission
from local to clouds is much more expensive than the computation in the clouds
itself. Towards this, 3D image compression techniques have been widely applied
to reduce the data traffic. However, most of the existing image compression
techniques are developed around human vision, i.e., they are designed to
minimize distortions that can be perceived by human eyes. In this paper we will
use deep learning based medical image segmentation as a vehicle and demonstrate
that interestingly, machine and human view the compression quality differently.
Medical images compressed with good quality w.r.t. human vision may result in
inferior segmentation accuracy. We then design a machine vision oriented 3D
image compression framework tailored for segmentation using DNNs. Our method
automatically extracts and retains image features that are most important to
the segmentation. Comprehensive experiments on widely adopted segmentation
frameworks with HVSMR 2016 challenge dataset show that our method can achieve
significantly higher segmentation accuracy at the same compression rate, or
much better compression rate under the same segmentation accuracy, when
compared with the existing JPEG 2000 method. To the best of the authors'
knowledge, this is the first machine vision guided medical image compression
framework for segmentation in the clouds.

本研究提出了一种基于深度学习的医学图像自动分割算法，通过专为分割设计的机器视觉态度的 3D 图像压缩框架，在保证分割精度的同时，显著提高了压缩比率。