Lossy compression in diagnostic virtual 3-dimensional microscopy--where is the limit?

Lossy Image Compression in a Preclinical Multimodal Imaging Study

The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

Assessing the quality of cytopathology whole slide imaging for education from archived cases

INTRODUCTION

Many institutions have cytopathology case archives for education. Unfortunately, these slides deteriorate over time and have limited accessibility. Whole slide imaging (WSI) can overcome these limitations. However, suboptimal image quality and scanning effort are barriers.

MATERIALS AND METHODS

We selected 123 slides from cytopathology study sets for WSI scanning at 400x magnification without z-stacking. The Ventana DP 200 scanner and Virtuoso software were used. Slides were scanned in 2 rounds: the first round of slides was prepared for scanning with light cleaning, and the second round was performed only on slides that had unacceptable WSI quality after thorough cleaning. Slides were assessed with a 4-tier grading system created by the authors. Time to scan each slide was recorded.

RESULTS

Within the first round, 96 of 123 (78%) slides scanned were determined to be of acceptable quality. After the second round of scanning, in total, 118 of 123 (95.9%) slides were determined to be of acceptable quality. The average time needed to scan each slide was 213 seconds.

CONCLUSIONS

The majority of slides scanned were of acceptable quality in the first round of scanning. After cleaning and rescanning, nearly every slide investigated was of acceptable quality. The primary objective is to provide other institutions that may be considering a similar project a benchmark so that they know what to expect in terms of slide scan success rate and the amount of time needed to digitize slides for educational archiving. This pilot study demonstrates the feasibility of using WSI for cytology education cases.

Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images

The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes.

An efficient architecture to support digital pathology in standard medical imaging repositories

In the past decade, digital pathology and whole-slide imaging (WSI) have been gaining momentum with the proliferation of digital scanners from different manufacturers. The literature reports significant advantages associated with the adoption of digital images in pathology, namely, improvements in diagnostic accuracy and better support for telepathology. Moreover, it also offers new clinical and research applications. However, numerous barriers have been slowing the adoption of WSI, among which the most important are performance issues associated with storage and distribution of huge volumes of data, and lack of interoperability with other hospital information systems, most notably Picture Archive and Communications Systems (PACS) based on the DICOM standard. This article proposes an architecture of a Web Pathology PACS fully compliant with DICOM standard communications and data formats. The solution includes a PACS Archive responsible for storing whole-slide imaging data in DICOM WSI format and offers a communication interface based on the most recent DICOM Web services. The second component is a zero-footprint viewer that runs in any web-browser. It consumes data using the PACS archive standard web services. Moreover, it features a tiling engine especially suited to deal with the WSI image pyramids. These components were designed with special focus on efficiency and usability. The performance of our system was assessed through a comparative analysis of the state-of-the-art solutions. The results demonstrate that it is possible to have a very competitive solution based on standard workflows.

Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

Background

Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.

Methods

Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.

Results

Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.

Conclusions

The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995

[Pathowiki. A free expert database for pathology]

The project Pathowiki (www.pathowiki.org) is a free expert database for texts, images, virtual slides and links to all subject areas of pathology in the internet. The aim of this project is to integrate all available information and media, in particular virtual microscopy, to achieve a fast overview of a relevant subject area. Here we present the project’s basic functions and applications and evaluate the project with respect to the ongoing digital developments in pathology.

Lossless compression of JPEG2000 whole slide images is not required for diagnostic virtual microscopy

The use of lossy compression in medical imaging is controversial, although it is inevitable to reduce large data amounts. In contrast with lossy compression, lossless compression does not impair image quality. In addition to our previous studies, we evaluated virtual 3-dimensional microscopy using JPEG2000 whole slide images of gastric biopsy specimens with or without Helicobacter pylori gastritis using lossless compression (1:1) or lossy compression with different compression levels: 5:1, 10:1, and 20:1. The virtual slides were diagnosed in a blinded manner by 3 pathologists using the updated Sydney classification. The results showed no significant differences in the diagnosis of H pylori between the different levels of compression in virtual microscopy. We assume that lossless compression is not required for diagnostic virtual microscopy. The limits of lossy compression in virtual microscopy without a loss of diagnostic quality still need to be determined. Analogous to the processes in radiology, recommendations for the use of lossy compression in diagnostic virtual microscopy have to be worked out by pathology societies.