Abstract
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.
Abstract (translated)
基于云的医学图像分析由于各种基于深度神经网络（DNN）的框架计算复杂度高，需要处理的医学图像量越来越大，近年来得到了广泛的应用。已经证明，对于医学图像来说，从本地到云端的传输比云端本身的计算要昂贵得多。为此，三维图像压缩技术被广泛应用于减少数据流量。然而，现有的大多数图像压缩技术都是围绕人类视觉发展起来的，即，它们的设计目的是尽量减少人类眼睛可以感知的失真。本文将以基于深度学习的医学图像分割为工具，说明机器和人对压缩质量的看法是不同的。医学图像经过高质量的W.R.T.人眼压缩后可能导致分割精度低下。然后，我们设计了一个面向机器视觉的三维图像压缩框架，该框架使用DNN进行分割。我们的方法自动提取和保留对分割最重要的图像特征。利用hvsmr 2016 challenge数据集对广泛采用的分割框架进行了综合实验，结果表明，与现有的jpeg 2000方法相比，我们的方法在相同的压缩率下能够获得更高的分割精度，或者在相同的分割精度下获得更好的压缩率。据作者所知，这是第一个用于云中分割的机器视觉引导医学图像压缩框架。
URL
https://arxiv.org/abs/1904.08487
