Annasamudram Nagasoujanya V, Okorie Azubuike M, Spencer Richard G, Kalyani Rita R, Yang Qi, Landman Bennett A, Ferrucci Luigi, Makrogiannis Sokratis
Delaware State University, Division of Physics, Engineering, Mathematics and Computer Science, Dover, Delaware, United States.
National Institutes of Health, National Institute on Aging, Baltimore, Maryland, United States.
J Med Imaging (Bellingham). 2024 Sep;11(5):054003. doi: 10.1117/1.JMI.11.5.054003. Epub 2024 Sep 3.
Segmentation is essential for tissue quantification and characterization in studies of aging and age-related and metabolic diseases and the development of imaging biomarkers. We propose a multi-method and multi-atlas methodology for automated segmentation of functional muscle groups in three-dimensional (3D) thigh magnetic resonance images. These groups lie anatomically adjacent to each other, rendering their manual delineation a challenging and time-consuming task.
We introduce a framework for automated segmentation of the four main functional muscle groups of the thigh, gracilis, hamstring, quadriceps femoris, and sartorius, using chemical shift encoded water-fat magnetic resonance imaging (CSE-MRI). We propose fusing anatomical mappings from multiple deformable models with 3D deep learning model-based segmentation. This approach leverages the generalizability of multi-atlas segmentation (MAS) and accuracy of deep networks, hence enabling accurate assessment of volume and fat content of muscle groups.
For segmentation performance evaluation, we calculated the Dice similarity coefficient (DSC) and Hausdorff distance 95th percentile (HD-95). We evaluated the proposed framework, its variants, and baseline methods on 15 healthy subjects by threefold cross-validation and tested on four patients. Fusion of multiple atlases, deformable registration models, and deep learning segmentation produced the top performance with an average DSC of 0.859 and HD-95 of 8.34 over all muscles.
Fusion of multiple anatomical mappings from multiple MAS techniques enriches the template set and improves the segmentation accuracy. Additional fusion with deep network decisions applied to the subject space offers complementary information. The proposed approach can produce accurate segmentation of individual muscle groups in 3D thigh MRI scans.
在衰老、与年龄相关及代谢性疾病的研究以及成像生物标志物的开发中，分割对于组织定量和特征描述至关重要。我们提出一种多方法和多图谱方法，用于对三维（3D）大腿磁共振图像中的功能性肌肉群进行自动分割。这些肌肉群在解剖学上彼此相邻，使得手动勾勒它们的轮廓成为一项具有挑战性且耗时的任务。
我们引入了一个框架，用于使用化学位移编码水脂磁共振成像（CSE-MRI）对大腿的四个主要功能性肌肉群，即股薄肌、腘绳肌、股四头肌和缝匠肌进行自动分割。我们建议将来自多个可变形模型的解剖映射与基于3D深度学习模型的分割相结合。这种方法利用了多图谱分割（MAS）的通用性和深度网络的准确性，从而能够准确评估肌肉群的体积和脂肪含量。
为了评估分割性能，我们计算了骰子相似系数（DSC）和95% Hausdorff距离（HD-95）。我们通过三倍交叉验证在15名健康受试者上评估了所提出的框架、其变体和基线方法，并在4名患者上进行了测试。多个图谱、可变形配准模型和深度学习分割的融合产生了最佳性能，所有肌肉的平均DSC为0.859，HD-95为8.34。
来自多种MAS技术的多个解剖映射的融合丰富了模板集并提高了分割准确性。与应用于个体空间的深度网络决策的额外融合提供了补充信息。所提出的方法可以在3D大腿MRI扫描中对各个肌肉群进行准确分割。
