A joint parcellation and boundary network with multi-rate-shared dilated graph attention for cortical surface parcellation
AbstractCortical surface parcellation aims to segment the surface into anatomically and functionally significant regions, which are crucial for diagnosing and treating numerous neurological diseases. However, existing methods generally ignore the difficulty in learning labeling patterns of boundaries, hindering the performance of parcellation. To this end, this paper proposes a joint parcellation and boundary network (JPBNet) to promote the effectiveness of cortical surface parcellation. Its core is developing a multi-rate-shared dilated graph attention (MDGA) module and incorporating boundary learning into the parcellatio...
Source: Medical and Biological Engineering and Computing - November 10, 2023 Category: Biomedical Engineering Source Type: research

Reliable and fast automatic artifact rejection of Long-Term EEG recordings based on Isolation Forest
AbstractLong-term electroencephalogram (Long-Term EEG) has the capacity to monitor over a long period, making it a valuable tool in medical institutions. However, due to the large volume of patient data, selecting clean data segments from raw Long-Term EEG for further analysis is an extremely time-consuming and labor-intensive task. Furthermore, the various actions of patients during recording make it difficult to use algorithms to denoise part of the EEG data, and thus lead to the rejection of these data. Therefore, tools for the quick rejection of heavily corrupted epochs in Long-Term EEG records are highly beneficial. I...
Source: Medical and Biological Engineering and Computing - November 9, 2023 Category: Biomedical Engineering Source Type: research

Deep learning-based 3D brain multimodal medical image registration
AbstractMedical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this paper, we propose an improved VoxelMorph network incorporating ResNet modules and CBAM (RCV-Net), for 3D multimodal unsupervised registration. Unlike popular convolution-based U-shaped registration networks like VoxelMorph, RCV-Net incorporates the convolutional block attention module (CBAM) during the convolution process. This inclusion enhances the feature map information...
Source: Medical and Biological Engineering and Computing - November 8, 2023 Category: Biomedical Engineering Source Type: research

Hybrid deep transfer learning-based early diagnosis of autism spectrum disorder using scalogram representation of electroencephalography signals
AbstractEarly diagnosis of autism spectrum disorder (ASD) plays an important role in the rehabilitation of the patient. This goal necessitates higher-level pattern representation and a strong modeling approach. The proposed approach applies scalogram images of electroencephalography signals for the first purpose and a two-level deep learning architecture for better classification. Scalogram images embed both the temporal and spectral information of the signal. On the other hand, the hybrid deep learning hierarchy of convolutional neural network followed by long short-term memory models both spatial and temporal information...
Source: Medical and Biological Engineering and Computing - November 8, 2023 Category: Biomedical Engineering Source Type: research

Evaluating sleep-stage classification: how age and early-late sleep affects classification performance
AbstractSleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using wavelets for feature extraction and random forest for classification, an automatic sleep-stage classification method was sought and assessed. The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier. From this study, we observed that these variables do affect the automatic model performance, improving the classif...
Source: Medical and Biological Engineering and Computing - November 6, 2023 Category: Biomedical Engineering Source Type: research

Instance-representation transfer method based on joint distribution and deep adaptation for EEG emotion recognition
AbstractElectroencephalogram (EEG) emotion recognition technology is essential for improving human –computer interaction. However, the practical application of emotion recognition technology is limited due to the variety of subjects and sessions. Transfer learning has been applied to address this issue and has received extensive research and application. Studies mainly concentrate on either ins tance transfer or representation transfer methods. This paper proposes an emotion recognition method called Joint Distributed Instances Represent Transfer (JD-IRT), which includes two core components: Joint Distribution Deep Adapt...
Source: Medical and Biological Engineering and Computing - November 2, 2023 Category: Biomedical Engineering Source Type: research

Nuclei detection in breast histopathology images with iterative correction
AbstractThis work presents a deep network architecture to improve nuclei detection performance and achieve the high localization accuracy of nuclei in breast cancer histopathology images. The proposed model consists of two parts, generating nuclear candidate module and refining nuclear localization module. We first design a novel patch learning method to obtain high-quality nuclear candidates, where in addition to categories, location representations are also added to the patch information to implement the multi-task learning process of nuclear classification and localization; meanwhile, the deep supervision mechanism is i...
Source: Medical and Biological Engineering and Computing - November 1, 2023 Category: Biomedical Engineering Source Type: research

Depicting and predicting changes of lung after lobectomy for cancer by using CT images
This study aimed to assess the lung and lobe change after lobectomy and predict the postoperative lung volume. The study included 135 lung cancer patients from two hospitals who underwent lobectomy (32, right upper lobectomy (RUL); 31, right middle lobectomy (RML); 24, right lower lobectomy (RLL); 26, left upper lobectomy (LUL); 22, left lower lobectomy (LLL)). We initially employ a convolutional neural network model (nnU-Net) for automatically segmenting pulmonary lobes. Subsequently, we assess the volume, effective lung volume (ELV), and attenuation distribution for each lobe as well as the entire lung, before and after ...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research

MBRARN: multibranch residual attention reconstruction network for medical image fusion
AbstractMedical image fusion aims to integrate complementary information from multimodal medical images and has been widely applied in the field of medicine, such as clinical diagnosis, pathology analysis, and healing examinations. For the fusion task, feature extraction is a crucial step. To obtain significant information embedded in medical images, many deep learning-based algorithms have been proposed recently and achieved good fusion results. However, most of them can hardly capture the independent and underlying features, which leads to unsatisfactory fusion results. To address these issues, a multibranch residual att...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research

Hardware deployment of deep learning model for classification of breast carcinoma from digital mammogram images
AbstractCancer is an illness that instils fear in many individuals throughout the world due to its lethal nature. However, in most situations, cancer may be cured if detected early and treated properly. Computer-aided diagnosis is gaining traction because it may be used as an initial screening test for many illnesses, including cancer. Deep learning (DL) is a CAD-based  artificial intelligence (AI) powered approach which attempts to mimic the cognitive process of the human brain. Various DL algorithms have been applied for breast cancer diagnosis and have obtained adequate accuracy due to the DL technology’s high featu...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research

Flexible endoscopic instrument for diagnosis and treatment of early gastric cancer
AbstractGastric cancer is a common cancer endangering human life and health worldwide. Early detection and diagnosis of gastric cancer that is normally performed by flexible endoscope can significantly improve the survival rate of patients. However, current endoscopic instruments have some problems, such as limitation of degrees of freedom (DOFs) and lack of surgical triangulation. Meanwhile, the lack of an intraoperative technique for the real-time evaluation of early gastric cancer is also a serious problem. To solve these problems, we have developed a dual-bending flexible endoscopic instrument for the diagnosis and tre...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research

Biomechanical effects of typical lower limb movements of Chen-style Tai Chi on knee joint
AbstractThe load and stress distribution on cartilage and meniscus of the knee joint in typical lower limb movements of Chen-style Tai Chi (TC) and deep squat (DS) were analyzed using finite element (FE) analysis. The loadings for this analysis consisted of muscle forces and ground reaction force (GRF), which were calculated through the inverse dynamic approach based on kinematics and force plate measurements obtained from motion capture experiments. Thirteen experienced practitioners performed four typical TC movements, namely, single whip (SW), brush knee and twist step (BKTS), stretch down (SD), and part the wild horse ...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research

Microstructural fatigue fracture behavior of glycated cortical bone
AbstractThe current study aims to simulate fatigue microdamage accumulation in glycated cortical bone with increased advanced glycation end-products (AGEs) using a phase field fatigue framework. We link the material degradation in the fracture toughness of cortical bone to the high levels of AGEs in this tissue. We simulate fatigue fracture in 2D models of cortical bone microstructure extracted from human tibias. The results present that the mismatch between the critical energy release rate of microstructural features (e.g., osteons and interstitial tissue) can alter crack initiation and propagation patterns. Moreover, the...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research

AEAU-Net: an unsupervised end-to-end registration network by combining affine transformation and deformable medical image registration
AbstractDeformable medical image registration plays an essential role in clinical diagnosis and treatment. However, due to the large difference in image deformation, unsupervised convolutional neural network (CNN)-based methods cannot extract global features and local features simultaneously and cannot capture long-distance dependencies to solve the problem of excessive deformation. In this paper, an unsupervised end-to-end registration network is proposed for 3D MRI medical image registration, named AEAU-Net, which includes two-stage operations, i.e., an affine transformation and a deformable registration. These two opera...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research

Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation
AbstractAccurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group...
Source: Medical and Biological Engineering and Computing - October 18, 2023 Category: Biomedical Engineering Source Type: research