Mr. Alexander I. Wright Profile

Alexander I. Wright

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Proceedings Article | 7 April 2023 Poster + Paper

Digital pathology whole slide image compression with vector quantized variational autoencoders

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

Proceedings Volume 12471, 124711B (2023) https://doi.org/10.1117/12.2647844

KEYWORDS: Image compression, Pathology, Tissues, Scanners, Medical imaging, Quantization

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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.

Proceedings Article | 20 March 2014 Paper

Towards automatic patient selection for chemotherapy in colorectal cancer trials

Alexander Wright, Derek Magee, Philip Quirke, Darren Treanor

Proceedings Volume 9041, 90410A (2014) https://doi.org/10.1117/12.2043220

KEYWORDS: Colorectal cancer, Tissues, Image processing, Cancer, Feature extraction, Visualization, Error control coding, Inspection, Pathology, Clinical trials

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A key factor in the prognosis of colorectal cancer, and its response to chemoradiotherapy, is the ratio of cancer cells to surrounding tissue (the so called tumour:stroma ratio). Currently tumour:stroma ratio is calculated manually, by examining H&E stained slides and counting the proportion of area of each. Virtual slides facilitate this analysis by allowing pathologists to annotate areas of tumour on a given digital slide image, and in-house developed stereometry tools mark random, systematic points on the slide, known as spots. These spots are examined and classified by the pathologist. Typical analyses require a pathologist to score at least 300 spots per tumour. This is a time consuming (10- 60 minutes per case) and laborious task for the pathologist and automating this process is highly desirable. Using an existing dataset of expert-classified spots from one colorectal cancer clinical trial, an automated tumour:stroma detection algorithm has been trained and validated. Each spot is extracted as an image patch, and then processed for feature extraction, identifying colour, texture, stain intensity and object characteristics. These features are used as training data for a random forest classification algorithm, and validated against unseen image patches. This process was repeated for multiple patch sizes. Over 82,000 such patches have been used, and results show an accuracy of 79%, depending on image patch size. A second study examining contextual requirements for pathologist scoring was conducted and indicates that further analysis of structures within each image patch is required in order to improve algorithm accuracy.

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