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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification

Amal Alshardan¹, Nazir Ahmad², Achraf Ben Miled³

¹Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports

|December 20, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep learning technique for accurately identifying mitotic nuclei (MN) in histopathological images, improving cancer grading. The COADL-MNSC method achieves high accuracy in segmenting and classifying these crucial cancer markers.

Keywords:

Capsule network Coati optimization algorithm HAU-UNet Median filtering Mitotic nuclei

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Medical image analysis
Computational pathology
Artificial intelligence in oncology

Background:

Accurate mitotic count (MC) is vital for cancer grading, traditionally performed manually by pathologists.
Manual MC assessment in histopathological images (HIs) is time-consuming and prone to variability.
Automated methods for mitotic nuclei (MN) segmentation and classification in HIs are needed to enhance diagnostic accuracy.

Purpose of the Study:

To propose a novel technique, the Coati Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Segmentation and Classification (COADL-MNSC), for automated MN detection in HIs.
To enhance the accuracy and efficiency of cancer grading by improving the segmentation and classification of MN.
To leverage deep learning models for precise feature extraction and classification of MN.

Main Methods:

Pre-processing of HIs using median filtering (MF).
Segmentation of MN using a Hybrid Attention Fusion U-Net (HAU-UNet) model.
Feature extraction using a capsule network (CapsNet) optimized by the Coati Optimization Algorithm (COA).
Classification of MN using a bidirectional long short-term memory (BiLSTM) model.

Main Results:

The COADL-MNSC methodology demonstrated excellent performance on an MN image dataset.
Achieved a superior accuracy of 98.89% compared to existing techniques across various metrics.
The integrated approach effectively segmented and classified mitotic nuclei.

Conclusions:

The proposed COADL-MNSC technique offers a highly accurate and efficient automated solution for mitotic nuclei detection in histopathological images.
This deep learning-based approach has the potential to significantly aid pathologists in cancer grading and diagnosis.
The study highlights the effectiveness of combining optimization algorithms with deep learning architectures for complex medical image analysis tasks.