Automatic Muscle Fiber Orientation Tracking in Ultrasound Images Using a New Adaptive Fading Bayesian Kalman Smoother

3D ultrasound-based determination of skeletal muscle fascicle orientations

Architectural parameters of skeletal muscle such as pennation angle provide valuable information on muscle function, since they can be related to the muscle force generating capacity, fiber packing, and contraction velocity. In this paper, we introduce a 3D ultrasound-based workflow for determining 3D fascicle orientations of skeletal muscles. We used a custom-designed automated motor driven 3D ultrasound scanning system for obtaining 3D ultrasound images. From these, we applied a custom-developed multiscale-vessel enhancement filter-based fascicle detection algorithm and determined muscle volume and pennation angle. We conducted trials on a phantom and on the human tibialis anterior (TA) muscle of 10 healthy subjects in plantarflexion (157 ± 7∘ ), neutral position (109 ± 7∘ , corresponding to neutral standing), and one resting position in between (145 ± 6∘ ). The results of the phantom trials showed a high accuracy with a mean absolute error of 0.92 ± 0.59∘ . TA pennation angles were significantly different between all positions for the deep muscle compartment; for the superficial compartment, angles are significantly increased for neutral position compared to plantarflexion and resting position. Pennation angles were also significantly different between superficial and deep compartment. The results of constant muscle volumes across the 3 ankle joint angles indicate the suitability of the method for capturing 3D muscle geometry. Absolute pennation angles in our study were slightly lower than recent literature. Decreased pennation angles during plantarflexion are consistent with previous studies. The presented method demonstrates the possibility of determining 3D fascicle orientations of the TA muscle in vivo.

Quantitative Muscle Fascicle Tractography Using Brightness-Mode Ultrasound

A muscle's architecture, defined as the geometric arrangement of its fibers with respect to its mechanical line of action, impacts its abilities to produce force and shorten or lengthen under load. Ultrasound and other noninvasive imaging methods have contributed significantly to our understanding of these structure-function relationships. The goal of this work was to develop a MATLAB toolbox for tracking and mathematically representing muscle architecture at the fascicle scale, based on brightness-mode ultrasound imaging data. The MuscleUS_Toolbox allows user-performed segmentation of a region of interest and automated modeling of local fascicle orientation; calculation of streamlines between aponeuroses of origin and insertion; and quantification of fascicle length, pennation angle, and curvature. A method is described for optimizing the fascicle orientation modeling process, and the capabilities of the toolbox for quantifying and visualizing fascicle architecture are illustrated in the human tibialis anterior muscle. The toolbox is freely available.

MDF-Net: A Multi-Scale Dynamic Fusion Network for Breast Tumor Segmentation of Ultrasound Images

Breast tumor segmentation of ultrasound images provides valuable information of tumors for early detection and diagnosis. Accurate segmentation is challenging due to low image contrast between areas of interest; speckle noises, and large inter-subject variations in tumor shape and size. This paper proposes a novel Multi-scale Dynamic Fusion Network (MDF-Net) for breast ultrasound tumor segmentation. It employs a two-stage end-to-end architecture with a trunk sub-network for multiscale feature selection and a structurally optimized refinement sub-network for mitigating impairments such as noise and inter-subject variation via better feature exploration and fusion. The trunk network is extended from UNet++ with a simplified skip pathway structure to connect the features between adjacent scales. Moreover, deep supervision at all scales, instead of at the finest scale in UNet++, is proposed to extract more discriminative features and mitigate errors from speckle noise via a hybrid loss function. Unlike previous works, the first stage is linked to a loss function of the second stage so that both the preliminary segmentations and refinement subnetworks can be refined together at training. The refinement sub-network utilizes a structurally optimized MDF mechanism to integrate preliminary segmentation information (capturing general tumor shape and size) at coarse scales and explores inter-subject variation information at finer scales. Experimental results from two public datasets show that the proposed method achieves better Dice and other scores over state-of-the-art methods. Qualitative analysis also indicates that our proposed network is more robust to tumor size/shapes, speckle noise and heavy posterior shadows along tumor boundaries. An optional post-processing step is also proposed to facilitate users in mitigating segmentation artifacts. The efficiency of the proposed network is also illustrated on the "Electron Microscopy neural structures segmentation dataset". It outperforms a state-of-the-art algorithm based on UNet-2022 with simpler settings. This indicates the advantages of our MDF-Nets in other challenging image segmentation tasks with small to medium data sizes.

Full Spatial Muscle Fiber Orientation Estimation From Ultrasound Images Using a Multitask Deformable Residual Neural Network

This paper proposes a multitask deformable residual neural network, for full spatial muscle fiber orientation (MFO) estimation from ultrasound (US) images. It is developed based on the state-of-the-art model of residual UNet (ResUNet), which combines the residual block and UNet for more efficient deep learning. To better capture the characteristics of curved muscle fibers in US images, deformable convolution is used to improve the conventional convolutions in ResUNet. Moreover, along with the detection of MFO, an extra task concerning muscle segmentation is assigned to the model in order to improve the detection accuracy and robustness. Experimental results on an inhouse dataset built upon 10 healthy human subjects demonstrate the superiority of the proposed model for full spatial MFO estimation from US images.

A clustering-based method for estimating pennation angle from B-mode ultrasound images

B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20 Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.

Automatic multi-plaque tracking and segmentation in ultrasonic videos

Carotid plaque tracking and segmentation in ultrasound videos is the premise for subsequent plaque property evaluation and treatment plan development. However, the task is quite challenging, as it needs to address the problems of poor image quality, plaque shape variations among frames, the existence of multiple plaques, etc. To overcome these challenges, we propose a new automatic multi-plaque tracking and segmentation (AMPTS) framework. AMPTS consists of three modules. The first module is a multi-object detector, in which a Dual Attention U-Net is proposed to detect multiple plaques and vessels simultaneously. The second module is a set of single-object trackers that can utilize the previous tracking results efficiently and achieve stable tracking of the current target by using channel attention and a ranking strategy. To make the first module and the second module work together, a parallel tracking module based on a simplified 'tracking-by-detection' mechanism is proposed to solve the challenge of tracking object variation. Extensive experiments are conducted to compare the proposed method with several state-of-the-art deep learning based methods. The experimental results demonstrate that the proposed method has high accuracy and generalizability with a Dice similarity coefficient of 0.83 which is 0.16, 0.06 and 0.27 greater than MAST (Lai et al., 2020), Track R-CNN (Voigtlaender et al., 2019) and VSD (Yang et al., 2019) respectively and has made significant improvements on seven other indicators. In the additional Testing set 2, our method achieved a Dice similarity coefficient of 0.80, an accuracy of 0.79, a precision of 0.91, a Recall 0.70, a F1 score of 0.79, an AP@0.5 of 0.92, an AP@0.7 of 0.74, and an expected average overlap of 0.79. Numerous ablation studies suggest the effectiveness of each proposed component and the great potential for multiple carotid plaques tracking and segmentation in clinical practice.

Ultrasound imaging to assess skeletal muscle architecture during movements: a systematic review of methods, reliability, and challenges

B-mode ultrasound is often used to quantify muscle architecture during movements. Our objectives were to 1) systematically review the reliability of fascicle length (FL) and pennation angles (PA) measured using ultrasound during movements involving voluntary contractions; 2) systematically review the methods used in studies reporting reliability, discuss associated challenges, and provide recommendations to improve the reliability and validity of dynamic ultrasound measurements; and 3) provide an overview of computational approaches for quantifying fascicle architecture, their validity, agreement with manual quantification of fascicle architecture, and advantages and drawbacks. Three databases were searched until June 2019. Studies among healthy human individuals aged 17-85 yr that investigated the reliability of FL or PA in lower-extremity muscles during isoinertial movements and that were written in English were included. Thirty studies (n = 340 participants) were included for reliability analyses. Between-session reliability as measured by coefficient of multiple correlations (CMC), and coefficient of variation (CV) was FL CMC: 0.89-0.96; CV: 8.3% and PA CMC: 0.87-0.90; CV: 4.5-9.6%. Within-session reliability was FL CMC: 0.82-0.99; CV: 0.0-6.7% and PA CMC: 0.91; CV: 0.0-15.0%. Manual analysis reliability was FL CMC: 0.89-0.96; CV: 0.0-15.9%; PA CMC: 0.84-0.90; and CV: 2.0-9.8%. Computational analysis FL CMC was 0.82-0.99, and PA CV was 14.0-15.0%. Eighteen computational approaches were identified, and these generally showed high agreement with manual analysis and high validity compared with phantoms or synthetic images. B-mode ultrasound is a reliable method to quantify fascicle architecture during movement. Additionally, computational approaches can provide a reliable and valid estimation of fascicle architecture.