Meta multi-task nuclei segmentation with fewer training samples.

Area of Science:

• Computational Biology
• Digital Pathology
• Machine Learning

Background:

• Cellular nuclei contain vital information about the microenvironment, crucial for biological and clinical research.
• Automatic nuclei segmentation aids pathologists and enables precise microenvironment analysis.
• Current deep learning models require extensive labeled data, hindering generalization to new domains due to annotation difficulties.

Purpose of the Study:

• To develop a generalized nuclei segmentation model with reduced data dependency and enhanced generalizability.
• To address the challenges of manual annotation in histopathology by proposing a few-shot learning approach.

Main Methods:

• A meta multi-task learning (Meta-MTL) framework incorporating model-agnostic meta-learning (MAML) as the outer optimization algorithm.
• An inner contour-aware multi-task learning model designed for nuclei segmentation.
• A feature fusion and interaction block (FFIB) to facilitate cross-task feature communication.

Main Results:

• The proposed Meta-MTL model demonstrates improved generalization capabilities for nuclei segmentation.
• Achieved comparable performance to state-of-the-art models using significantly fewer training samples.
• Showcased rapid adaptation to unseen domains with minimal manual annotations.

Conclusions:

• Meta-MTL offers an effective solution for nuclei segmentation with reduced data requirements.
• The model's ability to generalize and adapt quickly is beneficial for practical applications in digital pathology.
• This approach alleviates the burden of extensive data annotation in histopathological research.