Objective: To test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Background: Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal intramuscular states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction nor generalisation of independently varying, active and passive states. We use deep learning to investigate the generalizable content of 2D US muscle images. Method: US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle were recorded from 32 healthy participants (7 female, ages: 27.5, 19-65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, drift-free, components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous, independent variation of passive (joint angle) and active (electromyography) inputs. Results: For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography, and joint moment were estimated to accuracy 55+-8%, 57+-11%, and 46+-9% respectively. Significance: With 2D US imaging, deep neural networks can encode in generalizable form, the activity-length-tension state relationship of muscle. Observation only, low power, 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalised muscle diagnosis in pain, injury, neurological conditions, neuropathies, myopathies and ageing.
Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Surgery Duke University School of Medicine（杜克大学医学学院外科系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
Introducing NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies
Wellcome Centre for Mitochondrial Research（惠康线粒体研究中心）；Translational and Clinical Research Institute（转化与临床研究所）；Faculty of Medical Sciences（医学院）；Newcastle University（纽卡斯尔大学）；School of Computing（计算机学院）
Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders
IMT Atlantique（大西洋国立高等矿业电信学院）；Inserm（法国国家健康与医学研究院）；University Hospital of Brest（布雷斯特大学医院）；Fondation ILDYS（ILDYS基金会）；NIH（美国国立卫生研究院）
由于肌肉形状、大小、位置、纹理和损伤的巨大差异，肌肉骨骼疾病患者的病理性肩部肌肉的自动分割是一项具有挑战性的任务。一种可靠的全自动磁共振图像分割方法可以极大地帮助临床医生规划治疗干预和预测干预结果，同时消除耗时的手动分割工作。这项工作的目的有三个方面。首先，我们研究了使用深度学习技术进行病理性肩部肌肉分割的可行性，给出了非常有限的可用注释儿科数据。其次，我们解决了从健康到病理数据的学习可移植性，通过比较不同的学习方案的模型泛化。第三，提出了使用在非医疗数据上预训练的编码器的深度卷积编码器-解码器架构的扩展版本，以提高分割准确性。方法学方面的评价在留一出的方式对数据集的24个肩关节检查，从产科臂丛神经麻痹的患者，并集中在4个不同的肌肉，包括三角肌以及冈下肌，冈上肌和肩胛下肌从肩袖。最相关的分割模型在ImageNet上进行了部分预训练，并联合利用了患者间的健康和病理注释数据。三角肌、冈下肌、冈上肌和肩胛下肌的Dice评分分别为82.4%、82.0%、71.0%和82.8%。除冈上肌外，绝对表面估计误差均小于83 mm $^2$，$^2$为134.6 mm。这些贡献提供了新的视角，在肌肉骨骼疾病管理的背景下，力推理。
A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images
College of Science and Engineering, James Cook University（詹姆斯·库克大学理工学院）；McGill University（麦吉尔大学）；Element AI（Element AI公司）；College of Healthcare Sciences, James Cook University（詹姆斯·库克大学健康科学学院）
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report
École Centrale Paris（巴黎中央理工学院）；Université Paris-Est（巴黎东部大学）；École des Ponts ParisTech（巴黎路桥学院）；INRIA Saclay（法国国家信息与自动化研究所萨克莱分院）；Institute of Myology（肌病学研究所）；CEA（法国原子能和替代能源委员会）；UPMC University Paris 06（巴黎第六大学）
Deep Learning-Assisted Detection of Sarcopenia in Cross-Sectional Computed Tomography Imaging
Newcastle University（新castle大学）；The Newcastle upon Tyne Hospital NHS Foundation Trust（新castle upon Tyne医院 NHS基金会信托）；Technical University of Ostrava（奥斯特拉瓦技术大学）
Discriminative Parameter Estimation for Random Walks Segmentation
École Centrale Paris（巴黎中央理工学院）；Université Paris-Est（巴黎东大学）；École des Ponts ParisTech（巴黎高科桥路学院）；INRIA Saclay（法国国家信息与自动化研究所萨克雷中心）；Institute of Myology（肌病学研究所）；CEA（法国原子能和替代能源委员会）；UPMC University Paris 06（巴黎第六大学（UPMC））
Real-time interactive magnetic resonance (MR) temperature imaging in both aqueous and adipose tissues using cascaded deep neural networks for MR-guided focused ultrasound surgery (MRgFUS)
Korea University（高丽大学）；Korea Basic Science Institute（韩国基础科学研究院）；Korea Artificial Organ Center（韩国人工器官中心）；ICT Convergence Technology for Health and Safety（健康安全信息通信融合技术）；Department of Electronics and Information Engineering（电子与信息工程系）；Bioimaging Research Team（生物成像研究团队）
Combined fluorescence and photoacoustic imaging of tozuleristide in muscle tissue in vitro -- toward optically-guided solid tumor surgery: feasibility studies
本研究提出联合NIRF与快速扫描PAUS成像方法，在体外牛肌肉组织中实现了深达34 mm的20 uM tozuleristide识别，有效克服NIRF深度限制，为光学引导实体瘤手术的综合术前与术中检测及宽边缘切除提供了可行方案。
