# UltraCompression: Framework for High Density Compression of Ultrasound Volumes using Physics Modeling Deep Neural Networks

## TL;DR

This paper presents UltraCompression, a deep neural network framework that achieves ultra-high compression of ultrasound volumes while preserving speckle realism and essential tissue information, outperforming standard methods.

## Contribution

It introduces a novel physics-informed deep learning framework for ultrasound compression that maintains speckle realism at high compression ratios, with demonstrated superior segmentation accuracy.

## Key findings

- Achieves 725:1 compression ratio while preserving speckle distribution.
- Enables accurate tissue segmentation with a dice score of 0.89.
- Outperforms standard compression methods like JPEG and WebP.

## Abstract

Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the ability to retain realism of speckle appearance despite achieving very high-density compression factors. The compressor employs a tissue segmentation method, transmitting segments along with transducer frequency, number of samples and image size as essential information required for decompression. The decompressor is based on a convolutional network trained to generate patho-realistic ultrasound images which convey essential information pertinent to tissue pathology visible in the images. We demonstrate generalizability of the building blocks using two variants to build the compressor. We have evaluated the quality of decompressed images using distortion losses as well as perception loss and compared it with other off the shelf solutions. The proposed method achieves a compression ratio of $725:1$ while preserving the statistical distribution of speckles. This enables image segmentation on decompressed images to achieve dice score of $0.89 \pm 0.11$, which evidently is not so accurately achievable when images are compressed with current standards like JPEG, JPEG 2000, WebP and BPG. We envision this frame work to serve as a roadmap for speckle image compression standards.