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Related Experiment Video

Updated: Feb 27, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Gland segmentation in prostate histopathological images.

Malay Singh^1,2, Emarene Mationg Kalaw², Danilo Medina Giron³

¹National University of Singapore, School of Computing, Department of Computer Science, Singapore.

Journal of Medical Imaging (Bellingham, Wash.)

|June 28, 2017

Summary

An automated system accurately segments prostate cancer glands, reducing pathologist assessment variability. This tool aids in analyzing glandular structures for more consistent cancer malignancy grading.

Keywords:

AdaBoost digital pathology gland segmentation prostate cancer support vector machine

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Area of Science:

Digital pathology
Computational oncology
Medical image analysis

Background:

Accurate assessment of prostate cancer malignancy relies on identifying glandular structural features in tissue slides.
Manual gland observation is time-consuming and prone to interobserver variability, particularly for Gleason patterns 3 and 4.
Automated gland segmentation can provide objective analysis of glandular structures, potentially improving diagnostic consistency.

Purpose of the Study:

To develop and validate an automated gland segmentation system for prostate cancer tissue analysis.
To improve the accuracy and reduce variability in assessing prostate adenocarcinoma malignancy.
To assist pathologists by highlighting key glandular features for objective evaluation.

Main Methods:

Acquisition and manual annotation of 43 hematoxylin and eosin-stained prostate cancer tissue slide images.
Annotation included gland, lumen, periacinar retraction clefting, and stroma regions.
Development of an automated system using combined pixel and object-level classifiers with local and spatial information for segmentation.

Main Results:

The proposed automated gland segmentation system demonstrated superior performance compared to existing texture and gland structure-based methods.
The system effectively identified and segmented gland, lumen, clefting, and stroma regions.
Experimental results indicate good performance and potential for reducing interobserver variability.

Conclusions:

The automated gland segmentation system is a promising tool for objective analysis in digital pathology.
This technology can significantly decrease interobserver variability among pathologists in prostate cancer grading.
Objective highlighted patterns from automated segmentation can enhance the reliability of cancer malignancy assessment.