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Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Histopathological Image Deep Feature Representation for CBIR in Smart PACS.

Cristian Tommasino¹, Francesco Merolla², Cristiano Russo³

¹Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Via Claudio 21, Naples, 80125, Italy. cristian.tommasino@unina.it.

Journal of Digital Imaging

|June 9, 2023

Summary

This summary is machine-generated.

This study explores using Convolution Neural Networks (CNNs) to extract features from Whole Slide Images (WSIs) for better cancer diagnosis. The findings show promising results for computer-aided pathology information retrieval systems.

Keywords:

Computational pathology Content-based image retrieval Deep learning PACS

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Published on: October 17, 2018

Area of Science:

Digital pathology
Computational anatomy
Medical image analysis

Background:

Digitization of histology slides has led to a surge in Whole Slide Images (WSIs).
Effective archiving and retrieval systems are crucial for WSIs in cancer diagnosis and research.
Picture Archiving and Communication Systems (PACS) offer a solution for managing this data.

Purpose of the Study:

To develop a robust methodology for querying pathology data within PACS.
To investigate the effectiveness of Content-Based Image Retrieval (CBIR) for pathology image retrieval.
To explore novel approaches for feature extraction from WSIs for improved retrieval accuracy.

Main Methods:

Extracted features from Whole Slide Image (WSI) patches using pre-trained Convolutional Neural Networks (CNNs).
Evaluated features from different CNN layers and applied various dimensionality reduction techniques.
Conducted a qualitative analysis of the retrieval results.

Main Results:

Identified effective feature representations for WSIs using CNNs.
Demonstrated the potential of different CNN layers and dimensionality reduction techniques for image retrieval.
Achieved encouraging results for the proposed CBIR framework in pathology.

Conclusions:

The proposed framework shows promise for enhancing information retrieval in digital pathology.
Feature extraction from WSIs using CNNs is a viable approach for improving CBIR systems.
Further development of these methods can significantly aid cancer diagnosis and research.