Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks.

Area of Science:

• Digital pathology
• Computational oncology
• Medical image analysis

Background:

• Pathology image analysis tools, like automatic nuclei segmentation, face challenges due to data variability.
• Convolutional neural networks (CNNs) show promise in handling data variability for image analysis.
• The use of color normalization (CN) as a preprocessing step in CNN-based segmentation for digital pathology is inconsistent, lacking clear justification.

Purpose of the Study:

• To evaluate the necessity and impact of color normalization (CN) on CNN-based nuclei segmentation frameworks.
• To investigate the effect of CN on downstream processes in digital pathology image analysis.
• To provide a justified approach for utilizing CN in deep learning for cancer image analysis.

Main Methods:

• Evaluation of popular color normalization (CN) methods.
• Application of CN as a preprocessing step for CNN-based nuclei segmentation.
• Comparative analysis of segmentation performance with and without CN.

Main Results:

• Performance metrics of CNN-based nuclei segmentation were analyzed.
• The impact of different CN methods on segmentation accuracy was quantified.
• Variability in results based on the inclusion or exclusion of CN was observed.

Conclusions:

• The study provides empirical evidence on the effect of color normalization in CNN-based nuclei segmentation.
• Findings will guide researchers in deciding the necessity of CN for digital pathology image analysis.
• This research contributes to optimizing deep learning frameworks for cancer image segmentation.