A deep learning segmentation strategy that minimizes the amount of manually annotated images

Area of Science:

• Computational biology
• Medical imaging analysis
• Machine learning applications

Background:

• Deep learning, particularly convolutional neural networks, excels at image segmentation for biological objects.
• High performance in deep learning segmentation relies heavily on extensive, manually annotated training datasets.
• Manual annotation is a time-consuming bottleneck in developing accurate deep learning models for microscopy image analysis.

Purpose of the Study:

• To develop and evaluate a strategy for minimizing manual annotation effort in image segmentation.
• To optimize deep learning model performance with limited annotated data for biological applications.
• To define an optimal approach for nuclei segmentation in human precancerous polyp biopsy images.

Main Methods:

• Utilized an efficient, open-source annotation tool for streamlined labeling.
• Implemented data augmentation techniques to artificially expand the training dataset.
• Employed a conditional generative adversarial network (cGAN) to create synthetic training data.
• Combined semantic and instance segmentation approaches for comprehensive analysis.
• Evaluated the impact of each method on nuclei segmentation in 2D widefield microscopy images.

Main Results:

• Demonstrated that combining data augmentation, cGAN-generated data, and hybrid segmentation significantly reduces the need for extensive manual annotation.
• Quantified the performance improvement for nuclei segmentation using the proposed strategy.
• Identified the most impactful components of the strategy for efficient and accurate segmentation.

Conclusions:

• The presented strategy effectively minimizes manual annotation time for deep learning-based image segmentation.
• This approach enhances the feasibility of applying deep learning to biological image analysis, especially when large annotated datasets are unavailable.
• The findings provide a practical framework for optimizing nuclei segmentation in histopathological images.