# The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning

arXiv: https://arxiv.org/abs/2409.16733 · HTML: https://mlsanity.com/paper/2409.16733/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)

## Built on by (most cited first, 2 of 2)

- [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)
- [MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models](https://mlsanity.com/paper/2608.10291/) (2026)

## Abstract (arXiv)

Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet. While deep learning applications for natural images widely adopts the usage of lossy compression techniques, it is not widespread for 3D medical images. Using three CT datasets (17 tasks) and one MRI dataset (3 tasks) we demonstrate that lossy compression up to 20 times have no negative impact on segmentation quality with deep neural networks (DNN). In addition, we demonstrate the ability of DNN models trained on compressed data to predict on uncompressed data and vice versa with no quality deterioration.