Color quantification for evaluation of stained tissues

A New Automated Histomorphometric MATLAB Algorithm for Immunohistochemistry Analysis Using Whole Slide Imaging

The use of animal models along with the employment of advanced and sophisticated stereological methods for assessing bone quality combined with the use of statistical methods to evaluate the effectiveness of bone therapies has made it possible to investigate the pathways that regulate bone responses to medical devices. Image analysis of histomorphometric measurements remains a time-consuming task, as the image analysis software currently available does not allow for automated image segmentation. Such a feature is usually obtained by machine learning and with software platforms that provide image-processing tools such as MATLAB. In this study, we introduce a new MATLAB algorithm to quantify immunohistochemically stained critical-sized bone defect samples and compare the results with the commonly available Aperio Image Scope Positive Pixel Count (PPC) algorithm. Bland and Altman analysis and Pearson correlation showed that the measurements acquired with the new MATLAB algorithm were in excellent agreement with the measurements obtained with the Aperio PPC algorithm, and no significant differences were found within the histomorphometric measurements. The ability to segment whole slide images, as well as defining the size and the number of regions of interest to be quantified, makes this MATLAB algorithm a potential histomorphometric tool for obtaining more objective, precise, and reproducible quantitative assessments of entire critical-sized bone defect image data sets in an efficient and manageable workflow.

Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning

Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.

Influence of Texture and Colour in Breast TMA Classification

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors.

Protoporphyrin-IX fluorescence guided surgical resection in high-grade gliomas: The potential impact of human colour perception

INTRODUCTION

Protoporphyrin-IX (Pp-IX) fluorescence had been used frequently in recent years to guide microsurgical resection of high-grade gliomas (HGG), particularly following the publication of a randomized controlled trial demonstrating its advantages. However, Pp-IX fluorescence is dependent upon the surgeons' eyes' perception of red fluorescent colour. This study was designed to evaluate human eye fluorescence perception and establish a fluorescence scale.

MATERIALS AND METHODS

20 of 108 pre-recorded images from intraoperative fluorescence of HGG were used to construct an 8-panel visual analogue fluorescence scale. The scale was validated by testing 56 participants with normal colour vision and three red-green colour-blind participants. For intra-rater agreement ten participants were tested twice and for inter-observer reliability the whole cohort were tested.

RESULTS

The intra- and inter-observer reliability of the scale in normal colour vision participants was excellent. The scale was less reliable in the violet-blue panels of the scale. Colour-blind participants were not able to distinguish between red fluorescence and blue-violet colours.

CONCLUSION

The 8-panel fluorescence scale is valid in differentiating red, pink and blue colours in a fluorescence surgical field among participants with normal colour perception and potentially useful to standardize fluorescence-guided surgery. However, colourblind surgeons should not use fluorescence-guided surgery.

Magnetic resonance imaging tracking of ferumoxytol-labeled human neural stem cells: studies leading to clinical use

Numerous stem cell-based therapies are currently under clinical investigation, including the use of neural stem cells (NSCs) as delivery vehicles to target therapeutic agents to invasive brain tumors. The ability to monitor the time course, migration, and distribution of stem cells following transplantation into patients would provide critical information for optimizing treatment regimens. No effective cell-tracking methodology has yet garnered clinical acceptance. A highly promising noninvasive method for monitoring NSCs and potentially other cell types in vivo involves preloading them with ultrasmall superparamagnetic iron oxide nanoparticles (USPIOs) to enable cell tracking using magnetic resonance imaging (MRI). We report here the preclinical studies that led to U.S. Food and Drug Administration approval for first-in-human investigational use of ferumoxytol to label NSCs prior to transplantation into brain tumor patients, followed by surveillance serial MRI. A combination of heparin, protamine sulfate, and ferumoxytol (HPF) was used to label the NSCs. HPF labeling did not affect cell viability, growth kinetics, or tumor tropism in vitro, and it enabled MRI visualization of NSC distribution within orthotopic glioma xenografts. MRI revealed dynamic in vivo NSC distribution at multiple time points following intracerebral or intravenous injection into glioma-bearing mice that correlated with histological analysis. Preclinical safety/toxicity studies of intracerebrally administered HPF-labeled NSCs in mice were also performed, and they showed no significant clinical or behavioral changes, no neuronal or systemic toxicities, and no abnormal accumulation of iron in the liver or spleen. These studies support the clinical use of ferumoxytol labeling of cells for post-transplant MRI visualization and tracking.

Efficacy and Durability in Direct Labeling of Mesenchymal Stem Cells Using Ultrasmall Superparamagnetic Iron Oxide Nanoparticles with Organosilica, Dextran, and PEG Coatings

We herein report a comparative study of mesenchymal stem cell (MSC) labeling using spherical superparamagnetic iron oxide (SPIO) nanoparticles containing different coatings, namely, organosilica, dextran, and poly(ethylene glycol) (PEG). These nanomaterials possess a similar SPIO core size of 6–7 nm. Together with their coatings, the overall sizes are 10–15 nm for all SPIO@SiO₂, SPIO@dextran, and SPIO@PEG nanoparticles. These nanoparticles were investigated for their efficacies to be uptaken by rabbit bone marrow-derived MSCs without any transfecting agent. Experimentally, both SPIO@SiO₂ and SPIO@PEG nanoparticles could be successfully uptaken by MSCs while the SPIO@dextran nanoparticles demonstrated limited labeling efficiency. The labeling durability of SPIO@SiO₂ and SPIO@PEG nanoparticles in MSCs after three weeks of culture were compared by Prussian blue staining tests. SPIO@SiO₂ nanoparticles demonstrated more blue staining than SPIO@PEG nanoparticles, rendering them better materials for MSCs labeling by direct uptake when durable intracellullar retention of SPIO is desired.