Computational staining of unlabelled tissue

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Automated variable power cold microwave tissue processing: A novel universal tissue processing protocol without using formaldehyde and xylene

The tissue processing technique is used to preserve the biological structure of tissue samples harvested from biopsy as closely as possible to their in vivo state for the diagnoses and study of disease on the cellular level. This process includes fixation, dehydration, clearing, paraffin infiltration. The protocol follows paraffin block embedding, microtome slicing, staining, and microscope slide studying. Tissue processing is a time-sensitive task as histopathologists must rapidly prevent the decomposition of tissue samples and promptly provide diagnoses on time. However, there are different tissue processing protocols for processing tissues with different types and sizes. Fatty tissues and tissues thicker than 1.5 mm are more susceptible to human errors when choosing a proper tissue processing protocol when preparing separate batches of tissues. In this research, a novel automated variable power cold microwave tissue processor was developed using a universal tissue processing protocol (processing time of 97 min) to simultaneously process all tissue types up to 4 mm in thickness. The tissue processor operated with a relatively smaller number of reagents without formaldehyde or xylene. These materials cause severe health and safety issues for humans and the environment. The quality of healthy and diseased processed tissues (sizes 1×1×1 to 24×15×4 mm) of fatty, thyroid, breast, placenta, skin, prostate, stomach, and bladder was examined under a light microscope by defining MAS (morphology, artifacts, and staining) criteria for evaluating cellular details, tissue arrangement, tissue integrity, stain uptake, and visual distinction of a tissue structure in light microscopy. It was found that the new tissue processor has successfully processed both healthy and diseased fatty and nonfatty tissue samples, while all tissue samples also met MAS criteria. Light microscopy showed outstanding integrity and arrangement in the tissue structures with an excellent visual distinction.

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.