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Sequential Patching Lattice for Image Classification and Enquiry: Streamlining Digital Pathology Image Processing.

Areej Alsaafin¹, Peyman Nejat¹, Abubakr Shafique¹

¹KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota.

The American Journal of Pathology

|July 20, 2024

Summary

This summary is machine-generated.

We developed Sequential Patching Lattice for Image Classification and Enquiry (SPLICE), an unsupervised algorithm that creates compact representations of digital pathology whole-slide images (WSIs). SPLICE significantly reduces storage needs and improves computational efficiency for AI-driven analysis.

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Area of Science:

Computational pathology
Digital pathology
Artificial intelligence in histopathology

Background:

Digital pathology and AI are transforming histopathology.
Whole-slide images (WSIs) are large and complex, posing processing challenges.
Existing methods for WSI processing are often impractical or too expensive.

Purpose of the Study:

To introduce Sequential Patching Lattice for Image Classification and Enquiry (SPLICE), an unsupervised patching algorithm.
To condense histopathology WSIs into a compact set of representative patches, minimizing redundancy.
To improve the efficiency of image retrieval, processing, and analysis in digital pathology archives.

Main Methods:

Developed SPLICE, an unsupervised algorithm for WSI patching.
SPLICE sequentially analyzes WSIs to select high-quality, unique, and non-redundant features.
The algorithm creates a collage of representative patches from the WSI.

Main Results:

SPLICE demonstrated improved accuracy in search and match applications.
Achieved reduced computation time and storage requirements compared to existing methods.
Reduced storage requirements for tissue image representation by 50%.

Conclusions:

SPLICE offers an efficient method for representing histopathology WSIs.
The algorithm's efficiency enables broader application of computational pathology tools.
SPLICE facilitates the accelerated adoption of digital pathology.