Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.

Area of Science:

• Microscopy Image Analysis
• Computational Biology
• Deep Learning

Background:

• Cell and nucleus detection are crucial in microscopy image analysis.
• Supervised deep learning models, like Convolutional Neural Networks (CNNs), achieve high performance but require extensive annotated data.
• Data scarcity and the cost of re-annotating for new datasets hinder efficient analysis.

Purpose of the Study:

• To develop an efficient nucleus detection method for cross-modality microscopy images.
• To overcome the limitations of supervised learning by reducing the need for annotated data.
• To improve the throughput and accuracy of cell and nucleus detection in diverse imaging conditions.

Main Methods:

• A bidirectional, adversarial domain adaptation technique was developed for nucleus detection.
• The method employs both source-to-target and target-to-source image translation for nucleus detection.
• The unsupervised domain adaptation approach was extended to a semi-supervised learning framework to enhance performance.

Main Results:

• The proposed method demonstrated significant improvements in nucleus detection across three cross-modality microscopy datasets.
• The bidirectional adversarial domain adaptation achieved superior performance compared to baseline approaches.
• The semi-supervised version of the method showed competitive results against fully supervised models trained with complete target labels.

Conclusions:

• The developed domain adaptation method effectively addresses the challenges of nucleus detection in cross-modality microscopy data.
• This approach significantly enhances nucleus detection accuracy and efficiency, even with limited or no target domain annotations.
• The method offers a promising solution for robust and scalable cell and nucleus detection in various biological imaging applications.