Pathology Image Analysis Using Segmentation Deep Learning Algorithms.

Area of Science:

• Digital Pathology
• Medical Image Analysis
• Computational Biology

Background:

• Whole slide imaging (WSI) is increasingly routine in diagnostics, but efficient and accurate analysis remains challenging.
• Deep learning (DL) shows significant promise for pathology image analysis tasks like tumor identification and prognosis.
• Existing machine learning methods have been reviewed, but a focused review on DL for WSI segmentation is needed.

Purpose of the Study:

• To provide a detailed review of deep learning-based pathology image segmentation.
• To offer guidance for implementing DL in WSI analysis.
• To suggest future directions for improving segmentation performance.

Main Methods:

• Focus on deep learning algorithms, specifically segmentation models like fully convolutional networks (FCNs).
• Detailed description of the deep learning-based pathology image segmentation process.
• Review of current applications and performance of DL in WSI analysis.

Main Results:

• Deep learning algorithms, especially FCNs, demonstrate high accuracy, efficiency, and generalizability in pathology image segmentation.
• DL-based segmentation is a crucial tool for advancing WSI analysis.
• This review highlights the effectiveness of DL in addressing challenges in clinical diagnosis acceleration and automated image analysis.

Conclusions:

• Deep learning-based segmentation is a powerful tool for whole slide imaging analysis.
• This review serves as a guide for practitioners and researchers in digital pathology.
• Further improvements in segmentation performance are anticipated with continued DL advancements.