Dataset Information

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

ABSTRACT:

Motivation

Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy.

Results

In this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations.

Availability and implementation

Source code is available on Github at: https://github.com/Roth-Lab/ESQmodel.

SUBMITTER: Lee E

PROVIDER: S-EPMC10783950 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

Lee Eric E Lee Dongkyu D Fan Wayne W Lytle Andrew A Fu Yuxiang Y Scott David W DW Steidl Christian C Aparicio Samuel S Roth Andrew A

Bioinformatics (Oxford, England) 20240101 1

<h4>Motivation</h4>Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strateg ...[more]

PMID: 38152895

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Project description:Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial localisation and guidance in the analysis and characterisation of tumour micro-environment. Current segmentation approaches are based on supervised methods, which require extensive training data from high quality, manually annotated images. This is often difficult and costly to obtain. This paper presents an alternative data-independent framework based on unsupervised segmentation of oropharyngeal cancer tissue micro-arrays (TMAs). An automated segmentation algorithm based on mathematical morphology is first applied to light microscopy images stained with haematoxylin and eosin. This partitions the image into multiple binary 'virtual-cells', each enclosing a potential 'nucleus' (dark basins in the haematoxylin absorbance image). Colour and morphology measurements obtained from these virtual-cells as well as their enclosed nuclei are input into an advanced unsupervised learning model for the identification of epithelium and stromal tissues. Here we exploit two Consensus Clustering (CC) algorithms for the unsupervised recognition of tissue compartments, that consider the consensual opinion of a group of individual clustering algorithms. Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions. The proposed framework performance has been evaluated on fifty-five hand-annotated tissue images of oropharyngeal tissues. Qualitative and quantitative results of the proposed segmentation algorithm compare favourably with eight popular tissue segmentation strategies. Furthermore, the unsupervised results obtained here outperform those obtained with individual clustering algorithms.

Dataset Information

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

Motivation

Results

Availability and implementation

Publications

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

Similar Datasets

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