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Updated: Jul 5, 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

Multimodal brain tumor image segmentation based on DenseNet.

Xiaoqin Wu¹, Xiaoli Yang¹, Zhenwei Li¹

¹School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, P. R. China.

|January 18, 2024

Summary

This summary is machine-generated.

This study introduces an improved deep learning model for brain tumor segmentation in MRI scans. The novel approach enhances feature transmission and uses a mixed loss function for superior accuracy in diagnosing and treating brain tumors.

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Brain tumor segmentation is crucial for diagnosis and treatment planning.
Traditional U-net models face challenges with class imbalance and feature loss in multi-modal MRI.
Accurate segmentation requires robust algorithms that preserve critical information.

Purpose of the Study:

To develop an advanced network model for multi-modal brain tumor image segmentation.
To address class imbalance and feature information loss issues in existing methods.
To improve the accuracy and reliability of brain tumor segmentation algorithms.

Main Methods:

A novel network model combining U-net and DenseNet architectures was proposed.
Standard convolution blocks were replaced with dense blocks to enhance feature transmission.
A mixed loss function, incorporating Binary Cross Entropy and Tversky coefficient, was employed.

Main Results:

The proposed algorithm demonstrated significantly improved segmentation accuracy compared to U-Net, U-Net++, and PA-Net.
Dice coefficients reached 0.846 (WT), 0.861 (TC), and 0.782 (ET).
The algorithm showed superior performance in tumor core segmentation with a Sensitivity index of 0.924.

Conclusions:

The developed U-net and DenseNet combined model offers enhanced feature transmission and segmentation accuracy.
The mixed loss function effectively mitigates the impact of irrelevant features on segmentation.
This algorithm holds significant research and clinical value for brain tumor diagnosis and treatment.