Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems.

Area of Science:

• Digital Pathology
• Computational Pathology
• Medical Imaging

Background:

• The transition to digital pathology necessitates automated microscope scanning for efficient tissue sample digitization and diagnosis.
• Precise detection and segmentation of tissue regions are crucial for automated imaging in digital pathology.
• Current deep learning methods, like U-Net, face challenges due to the diversity and variability of tissue samples in training data.

Purpose of the Study:

• To address the limitations of training data in digital pathology by proposing a novel data augmentation technique.
• To enhance the robustness of deep learning models for tissue segmentation in the presence of diverse sample artifacts.

Main Methods:

• A data augmentation strategy was developed to artificially introduce artifact features into the training dataset.
• This method generates synthetic images by extending existing artifact features (e.g., pen markings, dirt, bubbles, stains) to the broader dataset.
• The approach was evaluated using U-Net convolutional neural networks for tissue segmentation.

Main Results:

• The proposed data augmentation method resulted in a 1-6% improvement in the F1 Score for samples with artifacts.
• The technique effectively generated synthetic data that improved model performance on challenging samples.
• This demonstrates the utility of synthetic data generation for improving deep learning in digital pathology.

Conclusions:

• Data augmentation by introducing synthetic artifacts is a viable strategy to improve deep learning model performance in digital pathology.
• This approach enhances the accuracy of tissue segmentation, particularly for challenging and diverse sample types.
• The method contributes to the advancement of automated slide scanning and diagnostic efficiency in digital pathology.