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.

本研究基于三维数值方案，比较了经典的演化模型、三维测地线模型和其一阶修改方案在低剂量 X 射线计算机断层扫描图像中用于腰肌分割的性能，结果表明这些基于梯度的方案相对于手动分割具有可靠性，而一阶方案所需的计算负担明显小于二阶方法。

X 光计算机断层扫描图像中腰肌分割的三维数值方案

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

Otago Exercise Program (OEP) is a rehabilitation program for older adults to
improve frailty, sarcopenia, and balance. Accurate monitoring of patient
involvement in OEP is challenging, as self-reports (diaries) are often
unreliable. With the development of wearable sensors, Human Activity
Recognition (HAR) systems using wearable sensors have revolutionized
healthcare. However, their usage for OEP still shows limited performance. The
objective of this study is to build an unobtrusive and accurate system to
monitor OEP for older adults. Data was collected from older adults wearing a
single waist-mounted Inertial Measurement Unit (IMU). Two datasets were
collected, one in a laboratory setting, and one at the homes of the patients. A
hierarchical system is proposed with two stages: 1) using a deep learning model
to recognize whether the patients are performing OEP or activities of daily
life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a
6-second sliding window to recognize the OEP sub-classes performed. The results
showed that in stage 1, OEP could be recognized with window-wise f1-scores over
0.95 and Intersection-over-Union (IoU) f1-scores over 0.85 for both datasets.
In stage 2, for the home scenario, four activities could be recognized with
f1-scores over 0.8: ankle plantarflexors, abdominal muscles, knee bends, and
sit-to-stand. The results showed the potential of monitoring the compliance of
OEP using a single IMU in daily life. Also, some OEP sub-classes are possible
to be recognized for further analysis.

通过使用腰部佩戴的惯性测量单元 (IMU) 进行数据收集，并采用深度学习模型，本研究提出了一个无障碍且准确的系统来监测老年人的 Otago 运动计划。研究结果表明，在实验室和家庭环境中，使用单个 IMU 可以对 Otago 运动计划及其子类进行准确的监测。