Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-19 DOI:10.1186/s12880-024-01423-0
Giulio Paolucci, Isabella Cama, Cristina Campi, Michele Piana
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Abstract

The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of computational pipelines for image processing that guarantee segmentation reliability and a significant degree of automation. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images. Specifically, here we focused on the level set methodology and compared the performances of two standard approaches, a classical evolution model and a three-dimension geodesic model, with the performances of an original first-order modification of this latter one. The results of this analysis show that these gradient-based schemes guarantee reliability with respect to manual segmentation and that the first-order scheme requires a computational burden that is significantly smaller than the one needed by the second-order approach.

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在 X 射线计算机断层扫描图像中分割腰肌的三维数值方案。
腰肌形态学和功能成像分析已被证明是评估 "肌肉疏松症 "的准确方法。"肌肉疏松症 "是指骨骼肌质量和功能的系统性丧失,可能与多种病因有关。要将肌肉疏松症评估纳入放射学工作流程,就必须实施图像处理计算管道,以保证分割的可靠性和高度自动化。本研究利用三维数值方案对低剂量 X 射线计算机断层扫描图像中的腰肌进行分割。具体来说,我们将重点放在水平集方法上,并比较了两种标准方法(经典演化模型和三维大地模型)的性能,以及后一种方法的原始一阶修改的性能。分析结果表明,这些基于梯度的方案保证了人工分割的可靠性,而且一阶方案所需的计算负担明显小于二阶方法。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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