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Updated: Sep 24, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Extending U-Net Network for Improved Nuclei Instance Segmentation Accuracy in Histopathology Images.

Gani Rahmon¹, Imad Eddine Toubal¹, Kannappan Palaniappan¹

¹Dept. of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.

IEEE Applied Imagery Pattern Recognition Workshop : [Proceedings]. IEEE Applied Imagery Pattern Recognition Workshop

|May 4, 2022

Summary

Accurate nuclei segmentation is crucial for disease analysis. A novel USE-Net model enhances segmentation by recalibrating features and outputting shape markers, improving accuracy even with limited data.

Keywords:

U-Net histopathology images nuclei segmentation squeeze and excitation

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Area of Science:

Computational pathology
Medical image analysis
Deep learning

Background:

Accurate nuclei segmentation is essential for analyzing tissue samples and predicting disease progression.
Current methods struggle with diverse nuclear morphologies and dense clustering, leading to annotation challenges and limited data for supervised learning.

Purpose of the Study:

To develop a robust nuclei segmentation method addressing limitations of current approaches.
To improve the separation of densely clustered nuclei and enhance segmentation accuracy.

Main Methods:

Utilized a U-Net architecture enhanced with Squeeze and Excitation (USE-Net) blocks for feature recalibration.
Extended the network to output shape markers alongside segmentation masks for improved nuclei separation.
Trained and evaluated the model on the 2018 MICCAI Multi-Organ-Nuclei-Segmentation (MoNuSeg) dataset.

Main Results:

Achieved promising nuclei segmentation results on unseen data.
Demonstrated robust performance despite a significantly small training dataset.
The USE-Net model effectively separated densely clustered nuclei.

Conclusions:

The proposed USE-Net offers a robust solution for nuclei instance segmentation in computational pathology.
The method shows potential for accurate analysis even with limited annotated data, overcoming common challenges in the field.