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Imaging Biological Samples with Optical Microscopy01:18

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LibMI: An Open Source Library for Efficient Histopathological Image Processing.

Yuxin Dong1, Pargorn Puttapirat1, Jingyi Deng1

  • 1School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Journal of Pathology Informatics
|October 12, 2020
PubMed
Summary
This summary is machine-generated.

LibMI is an open-source library and Modifiable Multiscale Image (MMSI) file format designed for efficient processing of whole-slide images (WSIs). This combination accelerates large image analysis and data sharing in digital pathology.

Keywords:
Extremely large imageimage processingopen formatwhole-slide image

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Area of Science:

  • Digital pathology
  • Computational biology
  • Image analysis

Background:

  • Whole-slide images (WSIs) present significant challenges in digital pathology due to their high resolution, hindering conventional data sharing and automated analysis.
  • Existing tools and file formats struggle to efficiently handle the large data volumes characteristic of WSIs.

Purpose of the Study:

  • To introduce LibMI, an open-source library, and its associated open and standardized image file format.
  • To enable efficient reading, writing, modification, and annotation of large-format histopathological images.

Main Methods:

  • LibMI employs a pyramid image structure and lazy propagation from a segment tree algorithm for linear time complexity in read/modify operations.
  • A cache mechanism is integrated to enhance program speed and efficiency.

Main Results:

  • LibMI demonstrates efficient histopathological image processing capabilities, including thresholding, color correction, and pixel-wise information storage on WSIs.
  • LibMI combined with the Modifiable Multiscale Image (MMSI) format shows a significant performance improvement, running 18.237 times faster on read-only tasks compared to existing solutions.

Conclusions:

  • The LibMI library and MMSI file format provide an efficient solution for reading and modifying WSIs, facilitating pixel-wise image processing pipelines for extremely large images.
  • The library and data schema are publicly available, promoting collaborative development and adoption in digital pathology research.