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

arXiv: https://arxiv.org/abs/1904.08487 · HTML: https://mlsanity.com/paper/1904.08487/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 9 of 9)

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- [Quantization of Deep Neural Networks for Accurate Edge Computing](https://mlsanity.com/paper/2104.12046/) (2021)
- [Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography](https://mlsanity.com/paper/2106.15597/) (2021)
- [Learning to Structure an Image with Few Colors and Beyond](https://mlsanity.com/paper/2208.08438/) (2022)
- [ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging](https://mlsanity.com/paper/2305.15617/) (2023)
- [Efficiency vs. Efficacy: Assessing the Compression Ratio-Dice Score Relationship through a Simple Benchmarking Framework for Cerebrovascular 3D Segmentation](https://mlsanity.com/paper/2510.03769/) (2025)
- [Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images](https://mlsanity.com/paper/2104.12044/) (2021)
- [Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System](https://mlsanity.com/paper/2212.08624/) (2022)

## Abstract (arXiv)

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.