Visual Prompting based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy...

Area of Science:

• Medical Imaging
• Computational Biology
• Artificial Intelligence

Background:

• Deep learning excels at semantic segmentation but requires large annotated datasets, which are scarce in medical imaging.
• Existing deep learning methods struggle with the data constraints inherent in medical image analysis.

Approach:

• Propose an incremental fine-tuning framework for multi-class segmentation of high-resolution multiplex immuno-fluorescence rat brain images.
• Utilize a modified Swin-UNet architecture for initial global segmentation (pre-training) on multiplex images.
• Employ incremental learning with minimal expert-provided labels to refine each segmentation class, optimizing performance for specific regions.

Key Points:

• The framework effectively handles limited annotated data typical in medical imaging research.
• Incremental learning allows experts to rapidly correct segmentation errors with targeted annotations.
• The proposed method demonstrates superior performance compared to traditional multi-class segmentation techniques.

Conclusions:

• This novel framework significantly reduces annotation effort and time for medical image segmentation.
• Achieves state-of-the-art performance in segmenting complex, high-resolution multiplex biological images.
• Offers a practical solution for applying deep learning to data-constrained medical image analysis problems.