Fast automatic segmentation of nuclei in microscopy images of tissue sections

Immune Cell and Cell Cluster Phenotyping, Quantitation, and Visualization Using In Silico Multiplexed Images and Tissue Cytometry

Phenotyping immune cells and cell clusters in situ, including their activation state and function, can aid in interpretation of spatial relationships within the tissue microenvironment. Immune cell phenotypes require multiple biomarkers. However, conventional microscopy setups can only image up to four biomarkers at one time. In this report, we describe and give an example of a workflow to phenotype, quantitate, and visualize greater than four biomarkers in silico utilizing multiplexed fluorescence histology and the TissueFAXS quantitative imaging system with a conventional microscopy setup. Biomarkers were conjugated to Cy3 or Cy5. Multiplexed staining was performed on formalin-fixed paraffin-embedded tissue sections. We imaged the slides, inactivated the dyes, and repeated the process until all biomarkers were stained. Phenotype profiles were built based on in silico combinations of the biomarkers. We used algorithms that aligned all images to create a composite image, isolated each cell in the image, and identified biomarker positive cells in the image. The in silico phenotypes were quantitated and displayed through flow cytometry-like histograms and dot scatterplots in addition to backgating into the tissue images. The advantage of our workflow is that it provides visual verification of cell isolation and identification as well as highlight characteristics of cells and cell clusters. © 2018 International Society for Advancement of Cytometry.

Supervised learning framework for screening nuclei in tissue sections

Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques is necessary for high throughput processing of such datasets. We describe a supervised learning framework, based on artificial neural networks (ANNs), to identify well-segmented nuclei in tissue sections from a multistage watershed segmentation algorithm. The successful automation was demonstrated by screening over 1400 well segmented nuclei from 9 datasets of human breast tissue section images and comparing the results to a previously used stacked classifier based analysis framework.

Automatic nuclei segmentation and spatial FISH analysis for cancer detection

Spatial analysis of gene localization using fluorescent in-situ hybridization (FISH) labeling is potentially a new method for early cancer detection. Current methodology relies heavily upon accurate segmentation of cell nuclei and FISH signals in tissue sections. While automatic FISH signal detection is a relatively simpler task, accurate nuclei segmentation is still a manual process which is fairly time consuming and subjective. Hence to use the methodology as a clinical application, it is necessary to automate all the steps involved in the process of spatial FISH signal analysis using fast, robust and accurate image processing techniques. In this work, we describe an intelligent framework for analyzing the FISH signals by coupling hybrid nuclei segmentation algorithm with pattern recognition algorithms to automatically identify well segmented nuclei. Automatic spatial statistical analysis of the FISH spots was carried out on the output from the image processing and pattern recognition unit. Results are encouraging and show that the method could evolve into a full fledged clinical application for cancer detection.