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Evaluation of Texture for Classification of Abdominal Aortic Aneurysm After Endovascular Repair

机译：血管内修复后腹部主动脉瘤分类的质地评价

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摘要

The use of the endovascular prostheses in abdominal aortic aneurysm has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long-term perspective, complications such as leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present, computed tomographic angiography (CTA) is the most common examination for medical surveillance. However, endoleak complications cannot always be detected by visual inspection on CTA scans. The investigation on new techniques to detect endoleaks and analyse their effects on treatment evolution is of great importance for endovascular aneurysm repair (EVAR) technique. The purpose of this work was to evaluate the capability of texture features obtained from the aneurysmatic thrombus CT images to discriminate different types of evolutions caused by endoleaks. The regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three techniques were applied to each ROI to obtain texture parameters, namely the grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRLM) and the grey level difference method (GLDM). The results showed that GLCM, GLRLM and GLDM features presented a good discrimination ability to differentiate between favourable or unfavourable evolutions. GLCM was the most efficient in terms of classification accuracy (93.41% ± 0.024) followed by GLRLM (90.17% ± 0.077) and finally by GLDM (81.98% ± 0.045). According to the results, we can consider texture analysis as complementary information to classified abdominal aneurysm evolution after EVAR.
机译：事实证明，在腹主动脉瘤中使用血管内假体是减少动脉瘤压力和破裂风险的有效技术。然而，从长远来看，可能会出现诸如动脉瘤囊内部泄漏（内漏）的并发症，从而引起压力升高并因此增加破裂的危险。目前，计算机断层血管造影（CTA）是医学监视中最常见的检查。但是，内窥镜检查并发症并非总是可以通过CTA扫描的目视检查发现。对检测内漏并分析其对治疗进展的影响的新技术的研究对于血管内动脉瘤修复（EVAR）技术至关重要。这项工作的目的是评估从动脉瘤血栓CT图像获得的纹理特征区分由内漏引起的不同类型演变的能力。经验丰富的放射科医生从EVAR后发展不同的患者中提取出感兴趣的区域（ROI）。将三种技术应用于每个ROI以获取纹理参数，即灰度共生矩阵（GLCM），灰度游程长度矩阵（GLRLM）和灰度差法（GLDM）。结果表明，GLCM，GLRLM和GLDM特征具有良好的区分能力，可以区分有利或不利的进化。就分类准确性而言，GLCM效率最高（93.41％±0.024），其次是GLLRM（90.17％±0.077），最后是GLDM（81.98％±0.045）。根据结果​​，我们可以将纹理分析视为EVAR后分类的腹部动脉瘤演变的补充信息。

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  • 来源
    《Journal of Digital Imaging》 |2012年第3期|p.369-376|共8页
  • 作者

    Guillermo García; Josu Maiora; Arantxa Tapia; Mariano De Blas;

  • 作者单位

    University of the Basque Country, Systems Engineering and Automatic Department, Polytechnical University College, Plaza Europa 1, 20018, San Sebastian, Spain;

    University of the Basque Country, Electronics and Telecommunications Department, Polytechnical University College, Plaza Europa 1, 20018, San Sebastian, Spain;

    University of the Basque Country, Systems Engineering and Automatic Department, Polytechnical University College, Plaza Europa 1, 20018, San Sebastian, Spain;

    Interventional Radiology Department, Donostia Hospital, Paseo Doctor José Beguiristain s, 20014, Donostia-San Sebastian, Spain;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Aneurysm; EVAR; Texture features; Neural network;

    机译：动脉瘤;EVAR;纹理特征;神经网络;

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