Deep learning-based compression of giga-resolution whole slide images

2026-05-17

AI Summary

This study addresses the challenge of high storage costs associated with whole-slide images (WSIs) in digital pathology by proposing a novel compression framework that integrates deep learning–based tissue segmentation with deep learning–driven image compression. The method first intelligently removes non-informative background regions from the slide and then employs a deep compression model in place of conventional encoders, further leveraging an image pyramid to enable multi-scale processing. To the best of our knowledge, this is the first work to synergistically combine intelligent background elimination with deep learning–based compression for WSIs. Experimental results demonstrate that the proposed approach achieves 35–40% storage savings in tissue regions and improves overall WSI compression ratios by 43–72% compared to JPEG family standards (JPEG, JPEG2000, JPEG-XL), with a combined strategy reaching up to 80% reduction while maintaining high-fidelity reconstruction (SSIM > 0.95).

Abstract

Implementation of digital pathology leads to an increased number of whole slide images (WSIs). The large size of WSIs is challenging. Today, WSIs are compressed with codecs like JPEG resulting in several gigabytes per WSI, and large amounts of space are wasted storing glass. In this study, deep learning-based tissue segmentation for glass removal, and deep learning compression methods were explored and compared with JPEG, JPEG-2000 and JPEG-XL. Image pyramids (N=21) with intact glass, glass replaced by single-colored pixels, and glass replaced by zero-byte tiles were created and compressed with JPEG, JPEG-XL and a deep learning model. Additionally, several compression models were evaluated on a tissue patch dataset and compared with JPEG, JPEG-2000 and JPEG-XL. Removing glass reduced file sizes considerably for JPEG and JPEG-XL. Deep learning-based image compression reduced the WSI size by 43-72% compared to JPEG compression, whereas deep learning-based glass removal reduced the WSI size by 0.3-33%, and 6-62% using only single-colored pixels and removing all-glass tiles, respectively. Combining the two gave a small improvement to a 44-80% total size reduction which indicates that deep learning-based image compression is able to efficiently compress glass tiles, whereas JPEG is not. On the tissue patch dataset, the best deep learning-based compression models saved on average ~35-40% per patch compared to JPEG, while keeping an average SSIM above 0.95, whereas JPEG-XL and JPEG-2000 saved 17% and 14%, respectively while keeping an SSIM of 0.96. However, the deep learning models had higher decompression times than JPEG and JPEG-XL.

Problem

• whole slide images
• image compression
• digital pathology
• storage efficiency
• glass removal

Innovation

• deep learning-based compression
• whole slide image (WSI)
• glass removal
• tissue segmentation
• image quality preservation