Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model

On the quantitative effects of compression of retinal fundus images on morphometric vascular measurements in VAMPIRE

BACKGROUND AND OBJECTIVES

This paper reports a quantitative analysis of the effects of joint photographic experts group (JPEG) image compression of retinal fundus camera images on automatic vessel segmentation and on morphometric vascular measurements derived from it, including vessel width, tortuosity and fractal dimension.

METHODS

Measurements are computed with vascular assessment and measurement platform for images of the retina (VAMPIRE), a specialized software application adopted in many international studies on retinal biomarkers. For reproducibility, we use three public archives of fundus images (digital retinal images for vessel extraction (DRIVE), automated retinal image analyzer (ARIA), high-resolution fundus (HRF)). We generate compressed versions of original images in a range of representative levels.

RESULTS

We compare the resulting vessel segmentations with ground truth maps and morphological measurements of the vascular network with those obtained from the original (uncompressed) images. We assess the segmentation quality with sensitivity, specificity, accuracy, area under the curve and Dice coefficient. We assess the agreement between VAMPIRE measurements from compressed and uncompressed images with correlation, intra-class correlation and Bland-Altman analysis.

CONCLUSIONS

Results suggest that VAMPIRE width-related measurements (central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), arteriolar-venular width ratio (AVR)), the fractal dimension (FD) and arteriolar tortuosity have excellent agreement with those from the original images, remaining substantially stable even for strong loss of quality (20% of the original), suggesting the suitability of VAMPIRE in association studies with compressed images.

Quantitative pixel intensity- and color-based image analysis on minimally compressed files: implications for whole-slide imaging

Current best practice in the quantitative analysis of microscopy images dictates that image files should be saved in a lossless format such as TIFF. Use of lossy files, including those processed with the JPEG algorithm, is highly discouraged due to effects of compression on pixel characteristics. However, with the growing popularity of whole-slide imaging (WSI) and its attendant large file sizes, compressed image files are becoming more prevelent. This prompted us to perform a color-based quantitative pixel analysis of minimally compressed WSI images. Sections from three tissues stained with one of three reagents representing the colors blue (hematoxylin), red (Oil-Red-O), and brown (immunoperoxidase) were scanned with a whole slide imager in triplicate at 20x, 40x, and 63x magnifications. The resulting files were in the form of a BigTIFF with a JPEG compression automatically applied during acquisition. Images were imported into analysis software, six regions of interest were applied to various morphological locations, and the areas assessed for the color of interest. Whereas the number of designated weakly or strongly positive pixels was variable across the triplicate scans for the individual regions of interest, the total number of positive pixels was consistent. These results suggest that total positivity for a specific color representing a histochemical or immunohistochemical stain can be adequately quantitated on compressed images, but degrees of positivity (e.g., weak vs. strong) may not be as reliable. However, it is important to assess individual whole-slide imagers, file compression level and algorithm, and analysis software for reproducibility.

Extracellular matrix composition defines an ultra-high-risk group of neuroblastoma within the high-risk patient cohort

Background:

Although survival for neuroblastoma patients has dramatically improved in recent years, a substantial number of children in the high-risk subgroup still die.

Methods:

We aimed to define a subgroup of ultra-high-risk patients from within the high-risk cohort. We used advanced morphometric approaches to quantify and characterise blood vessels, reticulin fibre networks, collagen type I bundles, elastic fibres and glycosaminoglycans in 102 high-risk neuroblastomas specimens. The Kaplan–Meier method was used to correlate the analysed elements with survival.

Results:

The organisation of blood vessels and reticulin fibres in neuroblastic tumours defined an ultra-high-risk patient subgroup with 5-year survival rate <15%. Specifically, tumours with irregularly shaped blood vessels, large sinusoid-like vessels, smaller and tortuous venules and arterioles and with large areas of reticulin fibres forming large, crosslinking, branching and haphazardly arranged networks were linked to the ultra-high-risk phenotype.

Conclusions:

We demonstrate that quantification of tumour stroma components by morphometric techniques has the potential to improve risk stratification of neuroblastoma patients.

Is it necessary to evaluate nuclei in HER2 FISH evaluation?

A new method that simplifies the evaluation of the traditional HER2 fluorescence in situ hybridization (FISH) evaluation in breast cancer was proposed. HER2 status was evaluated in digital images (DIs) captured from 423 invasive breast cancer stained sections. All centromeric/CEP17 and HER2 gene signals obtained from separated stacked DIs were manually counted on the screen. The global ratios were compared with the traditional FISH evaluation and the immunohistochemical status. The 2 FISH scores were convergent in 96.93% of cases, showing an "almost perfect" agreement with a weighted k of 0.956 (95% confidence interval, 0.928-0.985). The new method evaluates at least 3 times more nuclei than traditional methods and also has an almost perfect agreement with the immunohistochemical scores. The proposed enhanced method substantially improves HER2 FISH assessment in breast cancer biopsy specimens because the evaluation of HER2/CEP17 copy numbers is more representative, easier, and faster than the conventional method.

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

Digital image analysis: a review of reproducibility, stability and basic requirements for optimal results

Digital image analysis (DIA) is increasingly implemented in histopathological research to facilitate truly quantitative measurements, decrease inter-observer variation and reduce hands-on time. Originally, efforts were made to enable DIA to reproduce manually obtained results on histological slides optimized for light microscopy and the human eye. With improved technical methods and the acknowledgement that computerized readings are different from analysis by human eye, recognition has been achieved that to really empower DIA, histological slides must be optimized for the digital 'eye', with reproducible results correlating with clinical findings. In this review, we focus on the basic expectations and requirements for DIA to gain wider use in histopathological research and diagnostics. With a reference to studies that specifically compare DIA with conventional methods, this review discusses reproducibility, application of stereology-based quantitative measurements, time consumption, optimization of histological slides, regions of interest selection and recent developments in staining and imaging techniques.

Extending the knowledge in histochemistry and cell biology

Central to modern Histochemistry and Cell Biology stands the need for visualization of cellular and molecular processes. In the past several years, a variety of techniques has been achieved bridging traditional light microscopy, fluorescence microscopy and electron microscopy with powerful software-based post-processing and computer modeling. Researchers now have various tools available to investigate problems of interest from bird's- up to worm's-eye of view, focusing on tissues, cells, proteins or finally single molecules. Applications of new approaches in combination with well-established traditional techniques of mRNA, DNA or protein analysis have led to enlightening and prudent studies which have paved the way toward a better understanding of not only physiological but also pathological processes in the field of cell biology. This review is intended to summarize articles standing for the progress made in "histo-biochemical" techniques and their manifold applications.