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Related Experiment Video

Updated: Jul 4, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train

Amirreza Mahbod¹, Georg Dorffner², Isabella Ellinger³

¹Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria.

Computational and Structural Biotechnology Journal

|January 31, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep learning method to enhance nuclei instance segmentation in digital pathology images. The approach improves generalization across datasets, outperforming baseline models in accuracy.

Keywords:

Deep learning Digital pathology Machine learning Medical image analysis Normalization Nuclei segmentation

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Last Updated: Jul 4, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Digital Pathology
Computational Biology
Medical Image Analysis

Background:

Digital pathology enables automated whole slide imaging, driving demand for computerized image analysis.
Nuclei instance segmentation is critical for clinical and research applications in histopathology.
Deep learning (DL) models excel at nuclei segmentation but struggle with generalization to new datasets.

Purpose of the Study:

To develop a novel method that enhances the generalization capability of DL-based nuclei instance segmentation.
To improve the robustness of DL models when applied to unseen histopathological datasets.

Main Methods:

Utilized a state-of-the-art DL model as a baseline.
Incorporated non-deterministic, train-time stain normalization.
Implemented deterministic, test-time stain normalization.
Employed ensembling techniques to boost segmentation performance.
Trained the model on a single dataset and evaluated on seven diverse test datasets.

Main Results:

The proposed method demonstrated superior performance compared to the baseline model.
Achieved up to 4.9% improvement in Dice score.
Showed up to 5.4% improvement in aggregated Jaccard index.
Reported up to 5.9% improvement in panoptic quality score.

Conclusions:

The novel method significantly enhances the generalization of DL-based nuclei segmentation.
Stain normalization and ensembling are effective strategies for improving model robustness.
The approach offers improved accuracy for nuclei instance segmentation in digital pathology.