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

• URL: http://arxiv.org/abs/2409.16733v1
• Date: Wed, 25 Sep 2024 08:31:37 GMT
• Title: The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning
• Authors: Anvar Kurmukov, Bogdan Zavolovich, Aleksandra Dalechina, Vladislav Proskurov, Boris Shirokikh,
• Abstract summary: We show 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.
• Score: 39.97900702763419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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