Digital pathology whole slide image compression with vector quantized variational autoencoders

Jason Keighley; Marc de Kamps; Alexander Wright; Darren Treanor

doi:10.1117/12.2647844

6 April 2023 Digital pathology whole slide image compression with vector quantized variational autoencoders

Jason Keighley, Marc de Kamps, Alexander Wright, Darren Treanor

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711B (2023) https://doi.org/10.1117/12.2647844
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Digital pathology Whole Slide Images (WSIs) are large images (∼30 GB/slide uncompressed) of high resolution (0.25 microns per pixel), presenting a significant data storage challenge for hospitals wishing to adopt digital pathology. Lossy compression has been adopted by scanner manufacturers to address this issue - we compare lossy Joint Photographic Experts Group (JPEG) compression for WSIs and investigate the Vector Quantised Variational Autoencoder 2 variant (VQVAE2) as a possible alternative to reduce file size while encoding useful features in the compressed representation. We trained three VQVAE2 models on a Camelyon 2016 subset to the Compression Ratio (CR) of 19.2:1 (CR₁), 9.6:1 (CR₂) and 4.8:1 (CR₃) and tested on a Camelyon 2016 (DS₁) subset; University of California (DS₂) and Internal Validation Set (DS₃). We then compared compression performance to ImageMagick JPEG and JPEG 2000 implementations. Both JPEG and JPEG 2000 compression outperformed the VQVAE2 implementation within the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics. The trained VQVAE2 models could visually reproduce WSI tissue structure, but used colours from the original training data within the reconstructions on other datasets.

Citation Download Citation

Jason Keighley, Marc de Kamps, Alexander Wright, and Darren Treanor "Digital pathology whole slide image compression with vector quantized variational autoencoders", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711B (6 April 2023); https://doi.org/10.1117/12.2647844

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