Accurate segmentation of brain tumors from magnetic resonance 3D images (MRI) is critical for clinical decisions and surgical planning. Radiologists usually separate and analyze brain tumors by combining images of axial, coronal, and sagittal views. However, traditional convolutional neural network (CNN) models tend to use information from only a single view or one by one. Moreover, the existing models adopt a multi-branch structure with different-size convolution kernels in parallel to adapt to various tumor sizes. However, the difference in the convolution kernels’ parameters cannot precisely characterize the feature similarity of tumor lesion regions with various sizes, connectivity, and convexity. To address the above problems, we propose a hierarchical multi-view convolution method that decouples the standard 3D convolution into axial, coronal, and sagittal views to provide complementary-view features. Then, every pixel is classified by ensembling the discriminant results from the three views. Moreover, we propose a multi-branch kernel-sharing mechanism with a dilated rate to obtain parameter-consistent convolution kernels with different receptive fields. We use the BraTS2018 and BraTS2020 datasets for comparison experiments. The average Dice coefficients of the proposed network on the BraTS2020 dataset can reach 78.16%, 89.52%, and 83.05% for the enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively, while the number of parameters is only 0.5 M. Compared with the baseline network for brain tumor segmentation, the accuracy was improved by 1.74%, 0.5%, and 2.19%, respectively.

The intima-media thickness (IMT) of common carotid artery (CCA) can serve as an important indicator for the assessment of cardiovascular diseases (CVDs). In this paper an improved approach for automatic IMT measurement with low complexity and high accuracy is presented. 100 ultrasound images from 100 patients were tested with the proposed approach. The ground truth (GT) of the IMT was manually measured for six times and averaged, while the automatic segmented (AS) IMT was computed by the algorithm proposed in this paper. The mean difference ± standard deviation between AS and GT IMT is 0.0231 ± 0.0348 mm, and the correlation coefficient between them is 0.9629. The computational time is 0.3223 s per image with MATLAB under Windows XP on an Intel Core 2 Duo CPU E7500 @2.93 GHz. The proposed algorithm has the potential to achieve real-time measurement under Visual Studio.

Instance segmentation can help vehicles or robots enhance their understanding of a scene through the pixel-level segmentation of different objects. However, occlusion and boundary blur, especially in cases with similar colors or textures, are still challenges encountered in real-time robust segmentation tasks. To segment a complete instance boundary, the existing 2D approaches fuse local and abstract semantic features derived from the color domain, which leads to homogeneous semantic information, and efficiently separating different objects is difficult in some cases. To address these complicated scenes, inspired by a human prediction processing strategy, where “the brain fills in missing information in advance to help make better decisions”, this study proposes a real-time asymmetric dual-stream instance segmentation algorithm embedding a depth-predictive architecture that provides the covisible depth information of objects. Furthermore, a cross-domain data fusion method and an enhancement-decoupling loss are designed to complement RGB data by utilizing the rich foreground and boundary details of the predicted depth map. In addition, our model can be fine-tuned to integrate it with real depth domain data provided by different input devices. Extensive experiments conducted on the COCO, OCHuman and CityScapes datasets demonstrate the effectiveness of our method. We further deployed our DSDP method on a UAV platform for validation purposes and qualitatively confirmed its validity.

The development of automatic chest X-ray (CXR) disease classification algorithms is significant for diagnosing thoracic diseases. Owing to the characteristics of lesions in CXR images, including high similarity in appearance of the disease, varied sizes, and different occurrence locations, most existing convolutional neural network-based methods have insufficient feature extraction for thoracic lesions and struggle to adapt to changes in lesion size and location. To address these issues, this study proposes a high-resolution classification network with dynamic convolution and coordinate attention (HRCC-Net). In the method, this study suggests a parallel multi-resolution network in which a high-resolution branch acquires essential detailed features of the lesion and multi-resolution feature swapping and fusion to obtain multiple receptive fields to extract complicated disease features adequately. Furthermore, this study proposes dynamic convolution to enhance the network’s ability to represent multi-scale information to accommodate lesions of diverse scales. In addition, this study introduces a coordinate attention mechanism, which enables automatic focus on pathologically relevant regions and capturing the variations in lesion location. The proposed method is evaluated on ChestX-ray14 and CheXpert datasets. The average AUC (area under ROC curve) values reach 0.845 and 0.913, respectively, indicating this method’s advantages compared with the currently available methods. Meanwhile, with its specificity and sensitivity to measure the performance of medical diagnostic systems, the network can improve diagnostic efficiency while reducing the rate of misdiagnosis. The proposed algorithm has great potential for thoracic disease diagnosis and treatment.