Joint learning on diverse ultrasonic source scenarios presents a challenge in preserving stable gen-eralization due to the combination of heterogeneity of different sources and the inconsistency of joint learning features. Previous joint learning studies, which are not source-resilient frameworks, may not preserve stable generalization when trained on diverse source scenarios. Furthermore, the limited variations insingle-source data and the interference from ultrasound imaging, which are common in ultrasonic source scenarios, further decrease generalization. To address these problems, we pro posed a source-resilient joint learning framework consisting of three stages: 1) Source transforming, where our 1-to-N transformation unifies diverse source scenarios for source-resiliency. 2) Our feature enhancement modules model the source-resilient joint learning network, including a manifold-constraint normalization module (MCNM) for addressing heterogeneity by minimizing manifold-based loss, a task-consistent attention module (TCAM) shares the multi-scale features with self-attention to address inconsistency, and an adaptive feature-shifting module (AFSM) for feature-level augmentation to overcome single-source data.3) Our ultrasound-hybrid linear mapping (USmapping) cascades speckle randomization and mask-guiding Monge-Kantorovitch linear mapping to achieve ultrasonic style randomization for addressing the interference of ultrasonic data. Our framework was evaluated on eight ultrasound datasets from various scanners at multiple center sand surpassed previous comparable studies in both segmentation (DSCWAvgof 75.7%) and classification (AUROCWAvgof 68.8%) tasks. Our framework has the potential to serve as a general framework for enhancing the performance of joint learning under diverse ultrasonic source scenarios.

Plane-wave (PW) imaging, which provides high temporal resolution, has gained a significant attention. However, it is constrained by inherent lack of focus, requiring support from beamforming methods, such as Coherent Plane-Wave Compounding (CPWC), which can diminish the temporal advantages. Therefore, it is crucial to develop an advanced approach capable of addressing the trade-off between quality and frame rate. This paper proposes, for the first time, a hybrid progressive cross-domain fusion CNN-Transformer network (Hy-PCF), which is designed to simultaneously utilize both channel data and single-angle PW image for image reconstruction. Further, we design a novel Sparse Cross-Attention Guided Information Perception (SCAIP) module and use the single-angle PW image as guidance to extract information from the channel data. The qualitative and quantitative experimental findings demonstrate that Hy-PCF not only outperforms DAS significantly but also achieves performance comparable to the target CPWC. Hy-PCF represents a promising hybrid-CNN-Transformer network for PW imaging, offering a novel solution to enhance image quality, a critical advancement for clinical practice.

Differential diagnosis of tumors is important for computer-aided diagnosis. In computer-aided diagnosis systems, expert knowledge of lesion segmentation masks is limited as it is only used during preprocessing or as supervision to guide feature extraction. To improve the utilization of lesion segmentation masks, this study proposes a simple and effective multitask learning network that improves medical image classification using self-predicted segmentation as guiding knowledge; we call this network RS$^{2}$-net. In RS$^{2}$-net, the predicted segmentation probability map obtained from the initial segmentation inference is added to the original image to form a new input, which is then reinput to the network for the final classification inference. We validated the proposed RS$^{2}$-net using three datasets: the pNENs-Grade dataset, which tested the prediction of pancreatic neuroendocrine neoplasm grading, and the HCC-MVI dataset, which tested the prediction of microvascular invasion of hepatocellular carcinoma, and ISIC 2017 public skin lesion dataset. The experimental results indicate that the proposed strategy of reusing self-predicted segmentation is effective, and RS$^{2}$-net outperforms other popular networks and existing state-of-the-art studies. Interpretive analytics based on feature visualization demonstrates that the improved classification performance of our reuse strategy is due to the semantic information that can be acquired in advance in a shallow network.

Diffusion probabilistic models (DPM) can generate semantically valuable pixel-level representations and are widely used in medical image segmentation tasks. However, DPM faces challenges when dealing with medical image segmentation problems due to the irregular structure of medical images and the similarity between lesions and their surrounding environments. Therefore, this paper proposes a dual-branch Diff-UNet architecture to solve the medical image segmentation problem. Specifically, this architecture introduces the Transformer internal network on top of the standard UNet architecture based on DPM and realizes the interaction of UNet and Transformer branch features through bidirectional connection units to capture local features and remote dependencies better. In addition, through the feature fusion module (FFM), the global context information extracted by DPM is combined with the local detail features captured by the segmentation network. Simultaneously, this paper introduces a mutual graph learning (MGL) network to decompose the image into two task-specific feature maps, which are used to roughly locate the object position and capture the fine details of the object boundary. Finally, the cross attention (CA) module combines the edge information of the diffusion model with the features of the segmentation network to enhance the network’s ability to perceive images. Experiments demonstrate the effectiveness of our Diff-UNet on challenging datasets, including self-collected databases and LUNA16.

The accurate diagnosis of significant liver fibrosis ( ≥ F2) in patients with chronic liver disease (CLD) is critical, as ≥ F2 is a crucial factor that should be considered in selecting an antiviral therapy for these patients. This article proposes a handcrafted-feature-assisted deep convolutional neural network (HFA-DCNN) that helps radiologists automatically and accurately diagnose significant liver fibrosis from ultrasound (US) brightness (B)-mode images. The HFA-DCNN model has three main branches: one for automatic region of interest (ROI) segmentation in the US images, another for attention deep feature learning from the segmented ROI, and the third for handcrafted feature extraction. The attention deep learning features and handcrafted features are fused in the back end of the model to enable more accurate diagnosis of significant liver fibrosis. The usefulness and effectiveness of the proposed model were validated on a dataset built upon 321 CLD patients with liver fibrosis stages confirmed by pathological evaluations. In a fivefold cross validation (FFCV), the proposed model achieves accuracy, sensitivity, specificity, and area under the receiver-operating-characteristic (ROC) curve (AUC) values of 0.863 (95% confidence interval (CI) 0.820-0.899), 0.879 (95% CI 0.823-0.920), 0.872 (95% CI 0.800-0.925), and 0.925 (95% CI 0.891-0.952), which are significantly better than those obtained by the comparative methods. Given its excellent performance, the proposed HFA-DCNN model can serve as a promising tool for the noninvasive and accurate diagnosis of significant liver fibrosis in CLD patients.

Quantitative ultrasound (QUS), which attempts to extract quantitative features from the US radiofrequency (RF) or envelope data for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machine-learning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for fibrosis classification with the selected features, based on the results of which the optimal classifier was established. The performance of the proposed MP-QUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82%, and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24%, and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects.

Image-guided radiation therapy (IGRT) is the most effective treatment for head and neck cancer. The successful implementation of IGRT requires accurate delineation of organ-at-risk (OAR) in the computed tomography (CT) images. In routine clinical practice, OARs are manually segmented by oncologists, which is time-consuming, laborious, and subjective. To assist oncologists in OAR contouring, we proposed a three-dimensional (3D) lightweight framework for simultaneous OAR registration and segmentation. The registration network was designed to align a selected OAR template to a new image volume for OAR localization. A region of interest (ROI) selection layer then generated ROIs of OARs from the registration results, which were fed into a multiview segmentation network for accurate OAR segmentation. To improve the performance of registration and segmentation networks, a centre distance loss was designed for the registration network, an ROI classification branch was employed for the segmentation network, and further, context information was incorporated to iteratively promote both networks' performance. The segmentation results were further refined with shape information for final delineation. We evaluated registration and segmentation performances of the proposed framework using three datasets. On the internal dataset, the Dice similarity coefficient (DSC) of registration and segmentation was 69.7% and 79.6%, respectively. In addition, our framework was evaluated on two external datasets and gained satisfactory performance. These results showed that the 3D lightweight framework achieved fast, accurate and robust registration and segmentation of OARs in head and neck cancer. The proposed framework has the potential of assisting oncologists in OAR delineation.

Current clinical practice or radiomics studies of pancreatic neuroendocrine neoplasms (pNENs) require manual delineation of the lesions in computed tomography (CT) images, which is time-consuming and subjective. We used a semi-automatic deep learning (DL) method for segmentation of pNENs and verified its feasibility in radiomics analysis. This retrospective study included two datasets: Dataset 1, contrast-enhanced CT images (CECT) of 80 and 18 patients respectively collected from two centers; and Dataset 2, CECT of 56 and 16 patients respectively from two centers. A DL-based semi-automatic segmentation model was developed and validated with Dataset 1 and Dataset 2, and the segmentation results were used for radiomics analysis from which the performance was compared against that based on manual segmentation. The mean Dice similarity coefficient of the trained segmentation model was 81.8% and 74.8% for external validation with Dataset 1 and Dataset 2 respectively. Four classifiers frequently used in radiomics studies were trained and tested with leave-one-out cross-validation strategy. For pathological grading prediction with Dataset 1, the area under the receiver operating characteristic curve (AUC) with semi-automatic segmentation was up to 0.76 and 0.87 respectively for internal and external validation. For recurrence study with Dataset 2, the AUC with semi-automatic segmentation was up to 0.78. All these AUCs were not statistically significant from the corresponding results based on manual segmentation. Our study showed that DL-based semi-automatic segmentation is accurate and feasible for the radiomics analysis in pNENs.

This paper proposes a new algorithm for automatic estimation of muscle fiber orientation (MFO) in musculoskeletal ultrasound images, which is commonly used for both diagnosis and rehabilitation assessment of patients. The algorithm is based on a novel adaptive fading Bayesian Kalman filter (AF-BKF) and an automatic region of interest (ROI) extraction method. The ROI is first enhanced by the Gabor filter (GF) and extracted automatically using the revoting constrained Radon transform (RCRT) approach. The dominant MFO in the ROI is then detected by the RT and tracked by the proposed AF-BKF, which employs simplified Gaussian mixtures to approximate the non-Gaussian state densities and a new adaptive fading method to update the mixture parameters. An AF-BK smoother (AF-BKS) is also proposed by extending the AF-BKF using the concept of Rauch-Tung-Striebel smoother for further smoothing the fascicle orientations. The experimental results and comparisons show that: 1) the maximum segmentation error of the proposed RCRT is below nine pixels, which is sufficiently small for MFO tracking; 2) the accuracy of MFO gauged by RT in the ROI enhanced by the GF is comparable to that of using multiscale vessel enhancement filter-based method and better than those of local RT and revoting Hough transform approaches; and 3) the proposed AF-BKS algorithm outperforms the other tested approaches and achieves a performance close to those obtained by experienced operators (the overall covariance obtained by the AF-BKS is 3.19, which is rather close to that of the operators, 2.86). It, thus, serves as a valuable tool for automatic estimation of fascicle orientations and possibly for other applications in musculoskeletal ultrasound images.

In this study, an automatic muscle fiber orientation tracking approach based on Bayesian Kalman Filter (BKF) is proposed. The BKF employs a Gaussian mixture (GM) representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional Kalman filters (KFs) using GM. In this paper, the ultrasound image is firstly enhanced by a bank of Gabor Filters (GFs) based on the GM of the state density in BKF. Then, a bank of localized radon transforms (LRTs) are used to extract muscle fiber orientations and the dominant orientation is obtained by minimizing an energy function. Finally, the dominant orientation is fed back to the BKF as an observation. The performance of the proposed approach is compared with existing methods on five subjects over 1000+ clinical ultrasound images. Experimental results show that the proposed method can achieve accurate and robust measurements of fascicle orientation and outperforms all the existing methods.

The study is aimed to investigate in vivo behaviors of the rectus femoris muscle during isometric contraction by integrating simultaneously recorded electromyography (EMG), mechanomyography (MMG), and ultrasonography (US). We developed an experimental platform for simultaneous acquisition of EMG, MMG, US, as well as the torque, during isometric muscle contraction. Features from multimodal signals and images were then automatically extracted and calibrated to present time-varying characteristics of muscle behaviors. We further applied local polynomial regression (LPR) to reveal nonlinear and transient relationships between multimodal muscle features and torque. The results suggested that the proposed multimodal signal acquisition and integration are capable of providing novel and complete information about in vivo muscle contraction. The proposed experimental platform is a potentially useful tool for muscle assessment in various clinical and practical applications.