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Updated: Jan 11, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

严重性分类算法的建模使用腹腔大动脉动脉瘤计算机断层扫描图像细分基于U-Net与改进的降噪性能.

Sewon Lim¹, Hajin Kim¹, Kang-Hyeon Seo²

¹Department of Health Science, General Graduate School of Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea.

Sensors (Basel, Switzerland)

|November 13, 2025

概括

这项研究提高了CT扫描中的腹腔大动脉瘤 (AAA) 分段精度,使用中位数修改的维纳波器 (MMWF) 与U-Net. 货币市场基金显著改善了细分,并使精确的严重程度分类成为可能.

关键词:

这就是U-Net.腹部大动脉动脉瘤腹部大动脉动脉瘤中位数是修改过的维纳波器.降噪算法降噪算法降噪算法严重程度分类的严重程度分类.

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Last Updated: Jan 11, 2026

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Published on: July 5, 2024

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科学领域:

医疗成像医学成像
人工智能在医学中的应用
心血管成像 - 心血管成像

背景情况:

在计算机断层扫描 (CT) 图像中精确细分腹腔大动脉瘤 (AAA),对于诊断和治疗规划至关重要.
图像噪声在CT扫描中经常降低血管边界,阻碍精确的细分和影响临床决策.
U-Net是用于医学图像分割的流行的深度学习模型,但其性能对图像噪声敏感.

研究的目的:

评估各种消噪过器在改善腹腔大动脉动脉瘤 (AAA) 基于U-Net的细分方面的有效性.
评估降噪对AAA细分和随后的严重程度分类准确性的影响.
为了比较中位数修改的维纳波器 (MMWF) 与AAA图像分析的其他波器的性能.

主要方法:

松-高斯噪声被人为地引入到腹腔大动脉动脉瘤 (AAA) 的CT图像中.
几种无噪声过器,包括平均,中位数,维纳和中位数修改的维纳过器 (MMWF),应用于噪声图像.
在U-Net模型中,对无色化图像进行了细分,然后使用Hough圆算法测量直径,以进行严重性分类.

主要成果:

修改中位数的维纳波器 (MMWF) 显著提高了细分精度,改善了诸如子得分和雅卡德系数等关键指标.
与噪音图像相比,MMWF应用导致马修斯相关系数 (31.09%),子得分 (34.25%),贾卡德系数 (53.99%) 和平均表面距离 (3.70%) 的实质性改善.
基于MMWF处理的图像的严重性分类实现了100%的灵敏度,精度和准确性,优于其他过方法.

结论:

中位数修改的维纳波器 (MMWF) 在降低噪音和改进基于U-Net的腹腔大动脉动脉瘤 (AAA) 从CT图像的细分方面非常有效.
结合MMWF解密与U-Net细分和Hough圆算法分析,提供了一种可靠的方法,用于准确的AAA严重性分类.
这种方法具有显著的潜力,可以提高早期诊断和治疗计划的血管疾病,如AAA.