Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.

IF 2.2 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Tomography Pub Date : 2024-09-13 DOI:10.3390/tomography10090111

Xuzhi Zhao, Yi Du, Haizhen Yue

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Abstract

Background: The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images.

Methods: This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference.

Results: The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD.

Conclusion: The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

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低剂量计算机断层扫描中第三腰椎（L3）水平的骨骼肌分割：轻量级算法

背景：通过计算机断层扫描（CT）图像测量第三腰椎（L3）水平的骨骼肌横截面积是一种成熟的成像生物标志物，用于评估患者的营养状况。随着低剂量 CT 扫描在临床实践中的日益普及，在低剂量 CT 图像中准确、自动地分割第三腰椎水平的骨骼肌已成为一个需要解决的问题。本研究提出了一种轻量级算法，用于自动分割低剂量 CT 图像中 L3 层的骨骼肌：本研究纳入了 57 名直肠癌患者，这些患者均使用放射治疗 CT 扫描仪采集了低剂量普通和对比增强盆腔 CT 图像系列。随机选取 30 名患者作为训练集，用于开发轻量级分割算法，另外 27 名患者作为测试集。放射科医生为所有患者的两个图像系列都选择了最具代表性的 L3 水平轴向 CT 图像，三组观察者对测试集中 54 张 CT 图像中的骨骼肌进行了人工标注，作为金标准。从 Dice 相似性系数（DSC）、精确度、召回率、豪斯多夫距离第 95 百分位数（HD95）和平均表面距离（ASD）等方面评估了所提算法的性能。对所提算法的运行时间进行了记录。将基于深度学习的开源 AutoMATICA 算法与提出的算法进行了比较。观察者之间的差异也被用作参考：DSC、精确度、召回率、HD95、ASD 和运行时间分别为 93.2 ± 1.9%（平均值 ± 标准差）、96.7 ± 2.9%、90.0 ± 2.9%、4.8 ± 1.3 mm、0.8 ± 0.在 GPU 上，AutoMATICA 分别为 94.1 ± 4.1%、92.7 ± 5.5%、95.7 ± 4.0%、7.4 ± 5.7 mm、0.9 ± 0.6 mm 和 448 ± 40 ms。就平均 DSC、精确度、召回率、HD95 和 ASD 而言，所提算法与观察者间参考值的差异分别为 4.7%、1.2%、7.9%、3.2 毫米和 0.6 毫米：结论：所提出的算法可用于在普通或增强低剂量 CT 图像中分割 L3 层的骨骼肌。

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来源期刊

Tomography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

2.70

自引率

10.50%

发文量

222

期刊介绍： TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.