Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention

Area of Science:

• Histological pathology
• Medical imaging
• Artificial intelligence

Background:

• Frozen sections offer rapid intraoperative diagnosis but often lack nuclear detail and contain artifacts.
• Permanent sections provide superior diagnostic detail but require lengthy preparation.
• Accurate histological diagnosis is critical for surgical decision-making.

Purpose of the Study:

• To develop a generative deep learning method for enhancing frozen section images using permanent sections as guidance.
• To improve the diagnostic quality of frozen sections, particularly in the critical cell nuclei region.
• To accelerate the histological diagnosis workflow during surgery.

Main Methods:

• A segmented attention network was employed, leveraging nuclei-segmented images during training.
• A novel loss function was incorporated to refine nuclear details in the enhanced images.
• The approach was validated on diverse tissue types, including kidney, breast, and colon samples.

Main Results:

• The deep learning method successfully enhanced frozen section images, improving clarity and diagnostic detail.
• Enhancement focused on critical regions like cell nuclei, preserving existing features without generating artificial data.
• The process significantly improved histological efficiency, providing enhanced images within seconds.

Conclusions:

• This generative deep learning approach offers a significant advancement in histological image enhancement.
• The method improves diagnostic accuracy by enhancing crucial details in frozen sections.
• The technique seamlessly integrates into existing laboratory workflows, accelerating intraoperative diagnosis.