Medical & Biological Engineering & Computing最新文献

{"title":"Gait anomaly detection based on video-derived 3D pose estimation.","authors":"Lingling Chen, Ye Zheng, Zhuo Gong, Ding Wang","doi":"10.1007/s11517-025-03339-5","DOIUrl":"https://doi.org/10.1007/s11517-025-03339-5","url":null,"abstract":"<p><p>With the increase of age, the lower limb strength and function of the elderly gradually decline. Timely detection of motor dysfunction is of great significance for the prevention of disability, disease intervention, and improvement of living quality. Focusing on gait monitoring of the elderly living in groups, such as nursing homes, an abnormal gait recognition network based on daily walking information is proposed. We improve a multi-view 3D pose estimation network to extract gait parameters from the TUG exercise for monitoring, and design the abnormal gait recognition network to solve the problems of late evaluation of movement ability, large subjectivity, and the balance between accuracy and speed of the elderly living in groups. At a frame rate of 21.75 fps, the pose estimation accuracy is stable above 96.53%, and the joint error is controlled within 3.63°. In gait anomaly detection, the sensitivity reaches 96.71% and the inference speed reaches 512 ms; the F1 score reaches 0.9680, which is very close to the optimal value of the participant-comparison model, and the AUROC reaches 0.9694. This humble gait monitoring technology has great potential to provide assisted care and improve the overall well-being of the elderly.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GatedSegDiff: a gated fusion diffusion model for skin lesion segmentation.","authors":"Rui Wang, Liucheng Yao, Jiawen Zeng, Xiaofei Chen, Haiquan Wang, Chunhua Qian, Xiangyang Wang","doi":"10.1007/s11517-025-03337-7","DOIUrl":"https://doi.org/10.1007/s11517-025-03337-7","url":null,"abstract":"<p><p>Skin lesion segmentation is a vital process in skin disease diagnosis, crucial for maintaining diagnostic precision. Despite progress in existing image segmentation methods, challenges remain in handling the fuzzy boundaries of skin lesion areas. To address this, we developed GatedSegDiff-a dedicated end-to-end framework for melanoma skin lesion image segmentation. Innovatively integrating the semantic representation capabilities of denoising networks with a novel gated attention fusion module, this model effectively merges feature maps across various scales, enhancing segmentation precision. We evaluate our model on the ISIC 2017, ISIC 2018, and PH2 image datasets. For the IoU score, our model achieved an average increase of 4.3% across three datasets, while the HD95 score decreased by 1.5%. GatedSegDiff outperforms existing advanced methods across multiple performance metrics, showing significant progress in skin lesion segmentation tasks and validating its effectiveness within this specific domain. Impact statement-The GatedSegDiff model's innovative application in medical image segmentation, particularly in skin lesion segmentation, significantly enhances diagnostic precision and efficiency. By concentrating on information in lesion boundary areas, it substantially improves segmentation accuracy for lesions with fuzzy boundaries, which is crucial for the early diagnosis of serious skin diseases like melanoma. Additionally, it provides a solution to the shortcomings of general medical image segmentation methods in handling specific skin lesions, its applicability to other types of medical images requires further investigation. The model's outstanding performance on multiple skin lesion datasets highlights its potential for application in digital dermatological diagnosis, offering faster and more reliable services to patients, with significant implications for clinical use in the field of skin disease diagnosis. Melanin segmentation can be applied to medical integrated classification techniques to help experts select the most suitable treatment options for patients.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging.","authors":"Mario Mata-Castillo, Andrea Hernández-Villegas, Nelly Gordillo-Castillo, José Díaz-Román","doi":"10.1007/s11517-025-03345-7","DOIUrl":"https://doi.org/10.1007/s11517-025-03345-7","url":null,"abstract":"<p><p>Unruptured intracranial aneurysms are protuberances that appear in cerebral arteries, and their diagnostic evaluation can be a complex, time-consuming, and exhaustive task. In recent years, computer-aided systems have been developed to improve diagnostic processes. Although the proposed methods have already been reviewed to assess their suitability for clinical use, the segmentation methods have not been reviewed in detail, nor has there been a standardized way to compare segmentation and detection tasks. A systematic review was conducted to examine the technical and methodological factors contributing to this limitation. The analysis encompassed 49 studies conducted between 2019 and 2023 that utilized artificial intelligence methods and any medical imaging modality for the detection or segmentation of intracranial aneurysms. Most of the included studies focused exclusively on detection (57%), magnetic resonance angiography was the predominant imaging modality (47%), and the methodologies generally used 3D imaging as the input (71%). The reported sensitivities ranged from 0.68 to 0.90, specificities from 0.18 to 1.0, false positives per case from 0.18 to 13.8, and the Dice similarity coefficient from 0.53 to 0.98. Variations in aneurysm size were found to have a substantial impact on system performance. Studies were evaluated using a diagnostic accuracy study quality assessment tool, which revealed significant concerns regarding applicability. These concerns primarily stem from the poor reproducibility and inconsistent reporting of metrics. Recommendations for reporting outcomes were made to compare procedures across different types of imaging and tasks.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reimagining cancer tissue classification: a multi-scale framework based on multi-instance learning for whole slide image classification.","authors":"Zixuan Wu, Haiyong He, Xiushun Zhao, Zhenghui Lin, Yanyan Ye, Jing Guo, Wanming Hu, Xiaobing Jiang","doi":"10.1007/s11517-025-03341-x","DOIUrl":"https://doi.org/10.1007/s11517-025-03341-x","url":null,"abstract":"<p><p>In cancer pathology diagnosis, analyzing Whole Slide Images (WSI) encounters challenges like invalid data, varying tissue features at different magnifications, and numerous hard samples. Multiple Instance Learning (MIL) is a powerful tool for addressing weakly supervised classification in WSI-based pathology diagnosis. However, existing MIL frameworks cannot simultaneously tackle these issues. To address these challenges, we propose an integrated recognition framework comprising three complementary components: a preprocessing selection method, an Efficient Feature Pyramid Network (EFPN) model for multi-instance learning, and a Similarity Focal Loss. The preprocessing selection method accurately identifies and selects representative image patches, effectively reducing invalid data interference and enhancing subsequent model training efficiency. The EFPN model, inspired by pathologists' diagnostic processes, captures different tissue features in WSI images by constructing a multi-scale feature pyramid, enhancing the model's ability to recognize tumor tissue features. Additionally, the Similarity Focal Loss further improves the model's discriminative power and generalization performance by focusing on hard samples and emphasizing classification boundary information. The test accuracy for binary tumor classification on the CAMELYON16 and two private datasets reached 93.58%, 84.74%, and 99.91%, respectively, all of which outperform existing techniques.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new parallel-path ConvMixer neural network for predicting neurodegenerative diseases from gait analysis.","authors":"Jihen Fourati, Mohamed Othmani, Khawla Ben Salah, Hela Ltifi","doi":"10.1007/s11517-025-03334-w","DOIUrl":"https://doi.org/10.1007/s11517-025-03334-w","url":null,"abstract":"<p><p>Neurodegenerative disorders (NDD) represent a broad spectrum of diseases that progressively impact neurological function, yet available therapeutics remain conspicuously limited. They lead to altered rhythms and dynamics of walking, which are evident in the sequential footfall contact times measured from one stride to the next. Early detection of aberrant walking patterns can prevent the progression of risks associated with neurodegenerative diseases, enabling timely intervention and management. In this study, we propose a new methodology based on a parallel-path ConvMixer neural network for neurodegenerative disease classification from gait analysis. Earlier research in this field depended on either gait parameter-derived features or the ground reaction force signal. This study has emerged to combine both ground reaction force signals and extracted features to improve gait pattern analysis. The study is being carried out on the gait dynamics in the NDD database, i.e., on the benchmark dataset Physionet gaitndd. Leave one out cross-validation is carried out. The proposed model achieved the best average rates of accuracy, precision, recall, and an F1-score of 97.77 <math><mo>%</mo></math> , 96.37 <math><mo>%</mo></math> , 96.5 <math><mo>%</mo></math> , and 96.25 <math><mo>%</mo></math> , respectively. The experimental findings demonstrate that our approach outperforms the best results achieved by other state-of-the-art methods.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HADCN: a hierarchical ascending densely connected network for enhanced medical image segmentation.","authors":"Dibin Zhou, Mingxuan Zhao, Wenhao Liu, Xirui Gu","doi":"10.1007/s11517-025-03342-w","DOIUrl":"https://doi.org/10.1007/s11517-025-03342-w","url":null,"abstract":"<p><p>Medical image segmentation is a key component in computer-aided diagnostic technology. In the past few years, the U-shaped architecture-based hierarchical model has become the mainstream approach, which however often fails to provide accurate results due to the loss of detailed features. To address this issue, this paper proposes a hierarchical ascending densely connected network, called HADCNet, to capture both local short-range and global long-range pathological features in a hierarchically organized network for more accurate segmentation. First, HADCNet devises a cross-scale ascending densely connected structure with a multi-path attention gate (MAG) to achieve full-scale interaction of global pathological features. Then, spatial-channel reconstruction units (called SRU and CRU) are introduced to decrease redundant computation and facilitate representative feature learning. Finally, multi-scale outputs are aggregated for final imaging. Extensive experiments demonstrate that our method achieves an average DSC of 84.45% and HD95 of 17.55 mm on the Synapse dataset (for multi-organ segmentation), with a similarly impressive performance on the ACDC (for cardiac diagnosis) and ISIC2018 datasets (for lesion segmentation). Additionally, HADCNet can be flexibly incorporated into existing backbone networks for better performance, e.g., combining HADC with TransUnet and SwinUnet, respectively, leads to 3.28% and 2.53% Dice score improvements.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a gene expression classifier for breast cancer diagnosis.","authors":"Zahra Hosseinpour, Mostafa Rezaei-Tavirani, Mohammad-Esmaeil Akbari, Masoumeh Farahani","doi":"10.1007/s11517-025-03329-7","DOIUrl":"https://doi.org/10.1007/s11517-025-03329-7","url":null,"abstract":"<p><p>Breast cancer (BC) is the most common type of cancer in women worldwide. Solid tumors are complex structures composed of many cell types and extracellular matrix components. Understanding solid tumors is crucial for developing effective treatments. This study aimed to develop a gene expression classifier to predict BC with high accuracy. The study first identified the most important genes for cancer through differential expression analysis (DEA) between breast cancer and adjacent normal breast samples. The R package STRINGdb was then used to create a protein-protein interaction network (PPI) to examine upregulated genes and find clusters. Enrichment analyses were performed to identify overrepresented biological functions and pathways. A logistic regression prediction model was developed using a breast cancer dataset from TCGA and evaluated using discrimination and calibration measures. BUB1 expression in breast cancer was also investigated using quantitative analysis. Two significant clusters were identified, with cell cycle checkpoints and M phase key pathways in one cluster and extracellular matrix organization in the other. A prediction model using the hub gene set (COMP, FN1, SDC1, BUB1, TTK, and NUSAP1) showed high sensitivity (97.2%) and specificity (96.1%), and an AUC of 0.994. Three hub genes (COMP, FN1, and SDC1) were identified through the PPI network, strongly linked to extracellular matrix organization (BUB1, TTK, and NUSAP1) as hub genes involved in M phase and cell cycle checkpoints. Overall, the study identified hub pathways and genes that accurately distinguish between cancer and normal samples, presenting promising new possibilities for early cancer detection and improved BC therapy.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAMP-Net: a medical image segmentation network with split attention and multi-layer perceptron.","authors":"Xiaoxuan Ma, Sihan Shan, Dong Sui","doi":"10.1007/s11517-025-03331-z","DOIUrl":"https://doi.org/10.1007/s11517-025-03331-z","url":null,"abstract":"<p><p>Convolutional neural networks (CNNs) have achieved remarkable success in computer vision, particularly in medical image segmentation. U-Net, a prominent architecture, marked a major breakthrough and remains widely used in practice. However, its uniform downsampling strategy and simple stacking of convolutional layers in the encoder limit its ability to capture rich features at multiple depths, reducing its efficiency for rapid image processing. To address these limitations, this paper proposes a novel segmentation network that integrates attention mechanisms with multilayer perceptrons (MLPs). The network is designed to progressively capture and refine features at different levels. At the low-level layers, the primary feature conservation (PFC) block is introduced to preserve essential spatial details and reduce the loss of primary features during downsampling. In the mid-level layers, the compact attention block (CAB) enhances feature interaction through a multi-path attention structure, improving the network's ability to capture diverse semantic information. At the high-level layers, Shift MLP and Tokenized MLP blocks are incorporated. The Shift MLP block shifts feature channels along different axes, allowing for enhanced local feature modeling by focusing on specific regions of the convolutional features. The Tokenized MLP block converts these features into abstract tokens and leverages MLPs to model their representations in the latent space, effectively reducing the number of parameters and computational complexity while improving segmentation performance. The experiments conducted on the colorectal cancer tumor dataset CCI and the public dataset ISIC-2018 demonstrate that the proposed method significantly outperforms U-Net, U-Net++, Swin-U-Net, Attention U-Net, and RA-U-Net in terms of performance, with average improvements of 6.67%, 5.53%, 10.18%, 4.78%, and 3.55%, respectively. The code is available at the following link: https://github.com/QingTianer/SAMP-Net.git.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A direct learning approach for detection of hotspots in microwave hyperthermia treatments.","authors":"Hulusi Onal, Enes Girgin, Semih Doğu, Tuba Yilmaz, Mehmet Nuri Akinci","doi":"10.1007/s11517-025-03343-9","DOIUrl":"https://doi.org/10.1007/s11517-025-03343-9","url":null,"abstract":"<p><p>This paper presents a computational study for detecting whether the temperature values of the breast tissues are exceeding a threshold using deep learning (DL) during microwave hyperthermia (MH) treatments. The proposed model has a deep convolutional encoder-decoder architecture, which gets differential scattered field data as input and gives an image showing the cells exceeding the threshold. The data are generated by an in-house data generator, which mimics temperature distribution in the MH problem. The model is also tested with real temperature distribution obtained from electromagnetic-thermal simulations performed in commercial software. The results show that the model reaches an average accuracy score of 0.959 and 0.939 under 40 dB and 30 dB signal-to-noise ratio (SNR), respectively. The results are also compared with the Born iterative method (BIM), which is combined with some different conventional regularization methods. The results show that the proposed DL model outperforms the conventional methods and reveals the strong regularization capabilities of the data-driven methods for temperature monitoring applications.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmentation of lungs from chest X-ray images based on Deep Atrous Attention UNet (DAA-UNet) model.","authors":"Vivek Kumar Yadav, Jyoti Singhai","doi":"10.1007/s11517-025-03344-8","DOIUrl":"https://doi.org/10.1007/s11517-025-03344-8","url":null,"abstract":"<p><p>Medical image segmentation is a critical aspect of medical image analysis, particularly in the realm of medical image processing. While the UNet architecture is widely acknowledged for its effectiveness in medical image segmentation, it falls short in fully harnessing inherent advantages and utilising contextual data efficiently. In response, this research introduces an architecture named Deep Atrous Attention UNet (DAA-UNet), incorporating the attention module and Atrous Spatial Pyramid Pooling (ASPP) module in UNet. The primary objective is to enhance both efficiency and accuracy in the segmentation of medical images, with a specific focus on chest X-ray (CXR) images. DAA-UNet combines the integral features of UNet, ASPP, and attention mechanisms. The addition of an attention block improves the segmentation process by prioritising features from the encoding layer to the decoding layers. Our evaluation employs a tuberculosis dataset to assess the performance of the proposed model. The validation results demonstrate an average accuracy of 97.15%, an average Intersection over Union (IoU) value of 92.37%, and an average Dice Coefficient (DC) value of 93.25%. Notably, both qualitative and quantitative assessments for lung segmentation produce better outcomes than UNet and other relevant selected architectures.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}