Real-Time Ultrasound Segmentation, Analysis and Visualisation of Deep Cervical Muscle Structure

Abstract

Despite widespread availability of ultrasound and a need for personalised muscle diagnosis (neck/back pain-injury, work related disorder, myopathies, neuropathies), robust, online segmentation of muscles within complex groups remains unsolved by existing methods. For example, Cervical Dystonia (CD) is a prevalent neurological condition causing painful spasticity in one or multiple muscles in the cervical muscle system. Clinicians currently have no method for targeting/monitoring treatment of deep muscles. Automated methods of muscle segmentation would enable clinicians to study, target, and monitor the deep cervical muscles via ultrasound. We have developed a method for segmenting five bilateral cervical muscles and the spine via ultrasound alone, in real-time. Magnetic Resonance Imaging (MRI) and ultrasound data were collected from 22 participants (age: 29.0±6.6, male: 12). To acquire ultrasound muscle segment labels, a novel multimodal registration method was developed, involving MRI image annotation, and shape registration to MRI-matched ultrasound images, via approximation of the tissue deformation. We then applied polynomial regression to transform our annotations and textures into a mean space, before using shape statistics to generate a texture-to-shape dictionary. For segmentation, test images were compared to dictionary textures giving an initial segmentation, and then we used a customized Active Shape Model to refine the fit. Using ultrasound alone, on unseen participants, our technique currently segments a single image in [Formula: see text] to over 86% accuracy (Jaccard index). We propose this approach is applicable generally to segment, extrapolate and visualise deep muscle structure, and analyse statistical features online.

Automated analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning

“…This network has 32,092,992 free parameters and 19,872,665 ELU units. specifically developing methods for analysis of the neck muscles [22], [23]. Recently we contributed a dataset, a methodology for labelling training images suitable for participants with involuntary head movement, and a benchmark deep learning method for segmenting the neck muscles [23], [24].…”

“…US images (192 total, ~3 per participant) from the linked US-MRI dataset were labelled manually by annotating the boundaries around ten muscles, vertebra, ligamentum nuchae and skin. As described previously [22], [23], MRI images showing the same cod liver oil capsule marked plane were annotated and registered to the US images to guide annotation of the US images.…”

“…Image-labels from the linked US-MRI dataset were combined with a previous dataset of 25 linked US-MRI image-labels [22] and were augmented by reflection to provide a validation/test dataset of 434 examples, 86 participants. Participants were divided into two groups: for each neural network, one group was assigned to select the training iteration with lowest cost function (validation) and the selected network was tested by the other group, and vice versa for both networks.…”

“…palpation or surface electromyography, ultrasound can see muscles deep within the body. Segmentation is the foundation of muscle specific analysis and recent methods providing segmentation of the neck muscles include computer vision [22] and deep learning approaches [23]. This study applies the most recent iteration in deep learning methods developed by this group for this application.…”

Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks

“…Work-related upper limb and neck musculoskeletal disorders are one of the most common occupational disorders around the world [5] and the cost of these in the EU has been estimated to be between 0.5% and 2% of gross national product [5]. Personalised diagnosis requires available, non-invasive, accurate, objective measurement of function and condition for skeletal muscles throughout the body [2]- [4], [6], [7].…”