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Detection of lobular structures in normal breast tissue
Grégory Apou1, Nadine S Schaadt2, Benoît Naegel1
1ICube, University of Strasbourg, 300 bvd Sébastien Brant, 67412 Illkirch, France.
Insights
Automated methods effectively detect lobular structures in breast tissue whole slide images. Combining approaches enhances precision for immune cell analysis in biomarker research.
Area of Science:
- Computational pathology
- Digital pathology
- Biomedical image analysis
Background:
- Evaluating immune cells in histological sections is crucial for inflammatory condition research.
- Objective and consistent quantitative studies require automated detection of specific regions of interest (ROIs).
- This study focuses on automating the detection of lobular structures in human normal breast tissue whole slide images (WSIs).
Purpose of the Study:
- To compare different automated image analysis methods for detecting lobular structures in WSIs.
- To assess the feasibility and performance of various approaches for ROI detection in breast tissue.
- To provide insights for selecting appropriate automated ROI detection methods for biomarker research.
Main Methods:
- Three automated image analysis methods were evaluated on normal breast tissue from nine healthy patients.
- Methods included a bottom-up cell-based approach, a top-down texture classification approach, and deep learning-based texture classification.
- Immunohistochemically stained WSIs were used to detect lobular structures and analyze cell densities within ROIs.
Main Results:
- All three evaluated methods achieved comparable quality in automated lobular structure detection.
- Deep learning showed a minor advantage in sensitivity, texture classification in specificity, and the bottom-up approach in processing time.
- Combining the outputs from different approaches further improved the precision of lobular structure detection.
Conclusions:
- Automated detection of ROIs, specifically lobular structures, is feasible using various image analysis techniques.
- The choice of method should align with specific biomarker research needs regarding sensitivity, specificity, or speed.
- Detected ROIs can serve as a foundation for quantifying immune cell infiltration within lobular structures.
Background:
Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets.
Methods:
In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology.
Results:
All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision.
Conclusions:
Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures.
