Intelligent three-dimensional imaging in pathology: Applications and developments

Area of Science:

• Pathology
• Biomedical Imaging
• Computational Biology

Background:

• Conventional 2D pathology provides limited, potentially biased views of tissue structure.
• Loss of axial details in 2D imaging hinders accurate tumor and microenvironment assessment.

Purpose of the Study:

• To review technical pathways and applications of 3D pathology.
• To highlight the advantages of 3D over 2D pathology for clinical decision-making.
• To identify challenges and future directions for AI-enabled 3D pathology.

Main Methods:

• Tissue clearing and light-sheet fluorescence microscopy for volumetric data acquisition.
• Virtual staining, AI, and spatial omics for data analysis.
• Development of an analytical framework for 3D pathological data.

Main Results:

• 3D pathology enables volumetric analysis while preserving tissue architecture.
• Applications include enhanced tumor staging, lesion detection, and immune microenvironment profiling.
• 3D pathology shows potential for precision diagnosis, prognosis, and therapy selection.

Conclusions:

• AI-enabled 3D pathological imaging is a promising advancement in pathology.
• Integration into routine clinical practice requires further technological development and validation.
• Overcoming challenges in cost, standardization, and implementation is crucial for clinical adoption.