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.
Purpose: As pathology departments around the world contemplate digital microscopy for primary diagnosis, making an informed choice regarding display procurement is very challenging in the absence of defined minimum standards. In order to help inform the decision, we aimed to conduct an evaluation of displays with a range of technical specifications and sizes.
Approach: We invited histopathologists within our institution to take part in a survey evaluation of eight short-listed displays. Pathologists reviewed a single haematoxylin and eosin whole slide image of a benign nevus on each display and gave a single score to indicate their preference in terms of image quality and size of the display.
Results: Thirty-four pathologists took part in the display evaluation experiment. The preferred display was the largest and had the highest technical specifications (11.8-MP resolution, 2100 cd / m2 maximum luminance). The least preferred display had the lowest technical specifications (2.3-MP resolution, 300 cd / m2 maximum luminance). A trend was observed toward an increased preference for displays with increased luminance and resolution.
Conclusions: This experiment demonstrates a preference for large medical-grade displays with the high luminance and high resolution. As cost becomes implicated in procurement, significantly less expensive medical-grade displays with slightly lower technical specifications may be the most cost-effective option.
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.
Conference Committee Involvement (13)
Digital and Computational Pathology
18 February 2025 | San Diego, California, United States
Digital and Computational Pathology
19 February 2024 | San Diego, California, United States
Digital and Computational Pathology
20 February 2023 | San Diego, California, United States
Digital and Computational Pathology
20 February 2022 | San Diego, California, United States
Digital and Computational Pathology
15 February 2021 | Online Only, California, United States
Digital Pathology
19 February 2020 | Houston, Texas, United States
Digital Pathology
20 February 2019 | San Diego, California, United States
Digital Pathology
11 February 2018 | Houston, Texas, United States
Digital Pathology
12 February 2017 | Orlando, Florida, United States
Digital Pathology Posters
12 February 2017 | Orlando, FL, United States
Digital Pathology
2 March 2016 | San Diego, California, United States
Digital Pathology
25 February 2015 | Orlando, Florida, United States
Digital Pathology
16 February 2014 | San Diego, California, United States
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