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Updated: Jun 10, 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

多尺度输入层和密集解码器聚合网络用于从CT扫描中对COVID-19病变进行细分.

Xiaoke Lan¹, Wenbing Jin²

¹College of Internet of Things Technology, Hangzhou Polytechnic, Hangzhou, 311402, China. lxk@mail.hzpt.edu.cn.

Scientific reports

|October 10, 2024

概括

一个新的深度学习模型,MD-Net,在CT扫描中准确地细分COVID-19病变. 这种先进的网络通过有效分析复杂的图像细节和增强病变识别来提高诊断精度.

关键词:

在 COVID-19 疫情中,密集的解码器聚合集.多个尺度的输入层.这是SE-Convvv.分段化分段化分段化分段化这就是U-Net.

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

相关实验视频

Last Updated: Jun 10, 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

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

在医疗图像中精确细分COVID-19病变对于诊断和治疗至关重要.
挑战包括复杂的病变特征,微妙的组织差异和CT扫描中的图像噪声.

研究的目的:

设计一种新的深度学习架构,MD-Net,用于精确的COVID-19病变细分.
通过解决医疗图像分析的复杂性,提高细分精度.

主要方法:

开发了MD-Net,这是一个U形的深度学习网络,具有多级输入层 (MIL) 和密集解码器聚合 (DDA) 模块.
在编码器中内置了一个SE-Conv模块,用于增强功能识别和噪声抑制.

主要成果:

在Vid-QU-EX和QaTa-COV19-v2数据集上,MD-Net在细分COVID-19病变方面表现出卓越的表现.
与现有方法相比,在子值,马修斯相关系数 (Mcc) 和贾卡德指数方面获得了更高的分数.

结论:

MD-Net为COVID-19病变细分提供了一个强大而通用的解决方案.
拟议的架构有效地处理复杂的图像特征,从而提高诊断准确度.