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- Keighley, Jason, et al.
(author)
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Digital pathology whole slide image compression with Vector Quantized Variational Autoencoders
- 2023
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In: MEDICAL IMAGING 2023. - : SPIE-INT SOC OPTICAL ENGINEERING. - 9781510660472 - 9781510660489
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Conference paper (peer-reviewed)abstract
- Digital pathology Whole Slide Images (WSIs) are large images (similar to 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.
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