Medical & Biological Engineering & Computing最新文献_第2页

An arbitrary waveform neurostimulator for preclinical studies: design and verification. 用于临床前研究的任意波形神经刺激器：设计和验证。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-12-12 DOI: 10.1007/s11517-024-03241-6

Hipolito Guzman-Miranda, Alejandro Barriga-Rivera

{"title":"An arbitrary waveform neurostimulator for preclinical studies: design and verification.","authors":"Hipolito Guzman-Miranda, Alejandro Barriga-Rivera","doi":"10.1007/s11517-024-03241-6","DOIUrl":"10.1007/s11517-024-03241-6","url":null,"abstract":"Neural electrostimulation has enabled different therapies to treat a number of health problems. For example, the cochlear implant allows for recovering the hearing function and deep brain electrostimulation has been proved to reduce tremor in Parkinson's disease. Other approaches such as retinal prostheses are progressing rapidly, as researchers continue to investigate new strategies to activate targeted neurons more precisely. The use of arbitrary current waveform electrosimulation is a promising technique that allows exploiting the differences that exist among different neural types to enable preferential activation. This work presents a two-channel arbitrary waveform neurostimulator designed for visual prosthetics research. A field programmable gate array (FPGA) was employed to control and generate voltage waveforms via digital-to-analog converters. Voltage waveforms were then electrically isolated and converted to current waveforms using a modified Howland amplifier. Shorting of the electrodes was provided using multiplexers. The FPGA gateware was verified to a high level of confidence using a transaction-level modeled testbench, achieving a line coverage of 91.4%. The complete system was tested in saline using silver electrodes with diameters from 200 to 1000 µm. The bandwidth obtained was 30 kHz with voltage compliance ± 15 V. The neurostimulator can be easily scaled up using the provided in/out trigger ports and adapted to other applications with minor modifications.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1143-1159"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and feasibility study of a piezoresistive pressure sensor-based automated system for monitoring and controlling gastric pressure in endoscopy. 基于压阻式压力传感器的内窥镜胃压自动监测与控制系统的研制与可行性研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-12-03 DOI: 10.1007/s11517-024-03254-1

Sukgyu Koh, Sungwan Kim

{"title":"Development and feasibility study of a piezoresistive pressure sensor-based automated system for monitoring and controlling gastric pressure in endoscopy.","authors":"Sukgyu Koh, Sungwan Kim","doi":"10.1007/s11517-024-03254-1","DOIUrl":"10.1007/s11517-024-03254-1","url":null,"abstract":"Maintaining precise intragastric pressure during gastrointestinal endoscopy is critical for patient safety and diagnostic accuracy, yet current methods relying on manual adjustments pose risks of improper insufflation. This study aimed to develop an automated gastric pressure control system for flexible endoscopy, addressing these challenges with a piezoresistive pressure sensor that can be integrated into a 7.3 mm diameter flexible endoscope. The system, incorporating air and suction pumps controlled by a microcontroller, was calibrated in an acrylic chamber and validated through comprehensive testing in both an endoscopy simulator and a porcine specimen. Testing scenarios included normal breathing, coughing, belching, and combined events, assessing accuracy, stability, and real-time pressure regulation under conditions mimicking physiological responses. Results demonstrated high accuracy (R2 = 0.9999), minimal bias (0.23 mmHg), and strong agreement with reference standards, confirming effective pressure management. Simulated clinical scenarios in simulator and porcine specimen further showed the system's ability to maintain target pressure with minimal errors, indicating robustness under dynamic conditions. These findings suggest that the automated pressure control system significantly improves safety and procedural efficiency in endoscopy, with potential applicability to other minimally invasive procedures. Further animal model testing is recommended to validate the clinical performance under realistic physiological conditions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1101-1115"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11946989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss. 语义-空间特征融合皮层表面解析：具有边界对比损失的尺度统一空间学习网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-11-16 DOI: 10.1007/s11517-024-03242-5

Hailiang Ye, Siqi Liu, Ming Li, Houying Zhu, Feilong Cao

{"title":"Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss.","authors":"Hailiang Ye, Siqi Liu, Ming Li, Houying Zhu, Feilong Cao","doi":"10.1007/s11517-024-03242-5","DOIUrl":"10.1007/s11517-024-03242-5","url":null,"abstract":"The cortical surface parcellation provides prior guidance for studying mental disorders and human cognition. Graph neural networks (GNNs) have gained popularity in this task to preserve its spatial structure. However, previous GNNs struggled to effectively exploit the information contained in the complex spatial structure of the cortical surface and generally encountered an uneven node distribution issue. Meanwhile, labeling boundary nodes was also identified as a widespread problem in this task. Accordingly, this paper develops a scale-unified spatial learning network with a boundary contrastive loss (SSLNet) for cortical surface parcellation. Its core is the scale-unified spatial learning module. It devises neighbor feature extraction and aggregation strategies by fully integrating spatial coordinates and semantic structure to learn effective spatial features of local neighborhoods. More importantly, spatial scale unification is incorporated into this module to mitigate the negative effect on spatial learning caused by node distribution differences among local areas. Additionally, a universal boundary contrastive loss is constructed, enhancing the feature discriminability of boundary nodes by constraining them to be close to the same class nodes and apart from different class nodes in the feature space. It considerably improves boundary performance without increasing parameters or changing the network structure. Experiments regarding public Mindboggle demonstrate that the dice score and accuracy of SSLNet achieve <math><mrow><mn>89.8</mn> <mo>%</mo></mrow> </math> and <math><mrow><mn>90.89</mn> <mo>%</mo></mrow> </math> , respectively, surpassing existing methods.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"987-1000"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645093","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}

引用次数: 0

The influence of PEEK acetabular shell on the mechanical stability of total hip replacements under gait loading and motion. PEEK 髋臼壳对全髋关节置换术在步态负荷和运动下机械稳定性的影响。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-12-16 DOI: 10.1007/s11517-024-03257-y

Hongxing Shi, Xiaogang Zhang, Zhenxian Chen, Yali Zhang, Zhongmin Jin

{"title":"The influence of PEEK acetabular shell on the mechanical stability of total hip replacements under gait loading and motion.","authors":"Hongxing Shi, Xiaogang Zhang, Zhenxian Chen, Yali Zhang, Zhongmin Jin","doi":"10.1007/s11517-024-03257-y","DOIUrl":"10.1007/s11517-024-03257-y","url":null,"abstract":"The demand for total hip replacement surgery is increasing year by year. However, the issue of hip prosthesis failure, particularly the modular acetabular cup, still exists. The performance and functional requirements of modular acetabular cups have not yet met clinical expectations. This study focused on poly-ether-ether-ketone (PEEK) shells, using finite element methods to investigate their mechanical stability under gait loads and motion, including parameters such as deformation, micromotion, and bone strain. The results showed that a compromise was required among the mechanical performance, stability, and bone integration capabilities of the PEEK shell. As the shell rigidity increased, deformation decreased. However, increased rigidity also increased micromotion at the bone-prosthesis interface, reducing the area that promoted bone ingrowth. Additionally, potential bone absorption areas were also increased, reducing bone preservation and reconstruction capabilities. Compromises need to be made among mechanical performance, stability, and bone integration to achieve optimal mechanical stability. In this study, a 6 mm wall thickness PEEK shell was found to provide good overall mechanical stability.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1189-1200"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830680","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}

引用次数: 0

CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images. CC-TransXNet：用于从眼底图像自动分割视杯和视盘的混合 CNN 变换器网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-11-27 DOI: 10.1007/s11517-024-03244-3

Zhongzheng Yuan, Jinke Wang, Yukun Xu, Min Xu

{"title":"CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images.","authors":"Zhongzheng Yuan, Jinke Wang, Yukun Xu, Min Xu","doi":"10.1007/s11517-024-03244-3","DOIUrl":"10.1007/s11517-024-03244-3","url":null,"abstract":"Accurate segmentation of the optic disk (OD) and optic cup (OC) regions of the optic nerve head is a critical step in glaucoma diagnosis. Existing architectures based on convolutional neural networks (CNNs) still suffer from insufficient global information and poor generalization ability to small sample datasets. Besides, advanced transformer-based models, although capable of capturing global image features, perform poorly in medical image segmentation due to numerous parameters and insufficient local spatial information. To address the above two problems, we propose an innovative W-shaped hybrid network framework, CC-TransXNet, which combines the advantages of CNN and transformer. Firstly, by employing TransXNet and improved ResNet as feature extraction modules, the network considers local and global features to enhance its generalization ability. Secondly, the convolutional block attention module (CBAM) is introduced in the residual structure to improve the ability to recognize the OD and OC by applying attention in both the channel and spatial dimensions. Thirdly, the Contextual Attention (CoT) self-attention mechanism is used in the skip connection to adaptively allocate attention to the contextual information, further enhancing the segmentation's accuracy. We conducted experiments on four publicly available datasets (REFUGE 2, RIM-ONE DL, GAMMA, and Drishti-GS). Compared with the traditional U-Net, CNN, and transformer-based networks, our proposed CC-TransXNet improves the segmentation accuracy and significantly enhances the generalization ability on small datasets. Moreover, CC-TransXNet effectively controls the number of parameters in the model through optimized design to avoid the risk of overfitting, proving its potential for efficient segmentation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1027-1044"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142734360","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}

引用次数: 0

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification. 连续运动意象脑电分类的自适应会话增量广义学习系统。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-11-29 DOI: 10.1007/s11517-024-03246-1

Yufei Yang, Mingai Li, Linlin Wang

{"title":"An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.","authors":"Yufei Yang, Mingai Li, Linlin Wang","doi":"10.1007/s11517-024-03246-1","DOIUrl":"10.1007/s11517-024-03246-1","url":null,"abstract":"Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1059-1079"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752060","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}

引用次数: 0

Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases. Pan-Ret：可扩展的泛视网膜疾病检测半监督框架。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-12-14 DOI: 10.1007/s11517-024-03250-5

Rohan Banerjee, Rakhshanda Mujib, Prayas Sanyal, Tapabrata Chakraborti, Sanjoy Kumar Saha

{"title":"Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases.","authors":"Rohan Banerjee, Rakhshanda Mujib, Prayas Sanyal, Tapabrata Chakraborti, Sanjoy Kumar Saha","doi":"10.1007/s11517-024-03250-5","DOIUrl":"10.1007/s11517-024-03250-5","url":null,"abstract":"It has been shown in recent years that a range of optical diseases have early manifestation in retinal fundus images. It is becoming increasingly important to separate the regions of interest (RoI) upfront in the automated classification pipeline in order to ensure the alignment of the disease diagnosis with clinically relevant visual features. In this work, we introduce Pan-Ret, a semi-supervised framework which starts with locating the abnormalities in the biomedically relevant RoIs of a retinal image in an \"annotation-agnostic\" fashion. It does so by leveraging an anomaly detection setup using parallel autoencoders that are trained only on healthy population initially. Then, the anomalous images are separated based on the RoIs using a fully interpretable classifier like support vector machine (SVM). Experimental results show that the proposed approach yields an overall F1-score of 0.95 and 0.96 in detecting abnormalities on two different public datasets covering a diverse range of retinal diseases including diabetic retinopathy, hypertensive retinopathy, glaucoma, age-related macular degeneration, and several more in a staged manner. Thus, the work presents a milestone towards a pan-retinal disease diagnostic pipeline that can not only cater to the current set of disease classes, but has the capacity of adding further classes down the line. This is due to an anomaly detection style one-class learning setup of the deep autoencoder piece of the proposed pipeline, thus improving the generalizability of this approach compared to usual fully supervised competitors. This is also expected to increase the practical translational potential of Pan-Ret in a real-life scalable clinical setting.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"959-974"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822598","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}

引用次数: 0

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways. MediLite3DNet：一个用于鼻咽气道分割的轻量级网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-11-29 DOI: 10.1007/s11517-024-03252-3

Yanzhou Dai, Qiang Wang, Shulin Cui, Yang Yin, Weibo Song

{"title":"MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.","authors":"Yanzhou Dai, Qiang Wang, Shulin Cui, Yang Yin, Weibo Song","doi":"10.1007/s11517-024-03252-3","DOIUrl":"10.1007/s11517-024-03252-3","url":null,"abstract":"The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low computational efficiency when addressing this task. To overcome these issues, this paper introduces an innovative lightweight 3D medical image segmentation network-MediLite3DNet. The core of this network is the Parallel Multi-Scale High-Resolution Network (PMHNet), which effectively retains detailed features of the airway and optimizes the fusion of multi-scale features through its parallel structure. In response to the complexity of existing networks and their reliance on vast amounts of training data, this paper presents an efficient Hierarchical Decoupled Convolution Module (EHDC) to reduce computational costs while maintaining efficient feature extraction capabilities. Furthermore, to enhance the accuracy of segmentation, a lightweight Channel and Spatial Attention Mechanism (LCSA) is proposed. This mechanism identifies and emphasizes key channels and spatial features, improving the processing of complex medical images while controlling the increase in the number of parameters. Experiments conducted on a clinical CT dataset demonstrate the network's exceptional performance, with a Dice coefficient of 97.42%, sensitivity of 98.69%, and Jaccard index of 95%. Maintaining high precision, the model has a parameter count of only 0.227M and a floating-point operation count (FLOPs) of 24.526G, proving its computational efficiency. The significance of this study is that it provides a highly efficient and accurate diagnostic tool for children with adenoid hypertrophy. Additionally, with the innovative MediLite3DNet design, it brings a new lightweight solution to the domain of medical image segmentation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1081-1099"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752065","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}

引用次数: 0

CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data. CMFNet：基于 OCTA 数据进行精确血管分割的跨维模态融合网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-12-13 DOI: 10.1007/s11517-024-03256-z

Siqi Wang, Xiaosheng Yu, Hao Wu, Ying Wang, Chengdong Wu

{"title":"CMFNet: a cross-dimensional modal fusion network for accurate vessel segmentation based on OCTA data.","authors":"Siqi Wang, Xiaosheng Yu, Hao Wu, Ying Wang, Chengdong Wu","doi":"10.1007/s11517-024-03256-z","DOIUrl":"10.1007/s11517-024-03256-z","url":null,"abstract":"Optical coherence tomography angiography (OCTA) is a novel non-invasive retinal vessel imaging technique that can display high-resolution 3D vessel structures. The quantitative analysis of retinal vessel morphology plays an important role in the automatic screening and diagnosis of fundus diseases. The existing segmentation methods struggle to effectively use the 3D volume data and 2D projection maps of OCTA images simultaneously, which leads to problems such as discontinuous microvessel segmentation results and deviation of morphological estimation. To enhance diagnostic support for fundus diseases, we propose a cross-dimensional modal fusion network (CMFNet) using both 3D volume data and 2D projection maps for accurate OCTA vessel segmentation. Firstly, we use different encoders to generate 2D projection features and 3D volume data features from projection maps and volume data, respectively. Secondly, we design an attentional cross-feature projection learning module to purify 3D volume data features and learn its projection features along the depth direction. Then, we develop a cross-dimensional hierarchical fusion module to effectively fuse coded features learned from the volume data and projection maps. In addition, we extract high-level semantic weight information and map it to the cross-dimensional hierarchical fusion process to enhance fusion performance. To validate the efficacy of our proposed method, we conducted experimental evaluations using the publicly available dataset: OCTA-500. The experimental results show that our method achieves state-of-the-art performance.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1161-1176"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819936","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}

引用次数: 0

Multi-type stroke lesion segmentation: comparison of single-stage and hierarchical approach. 多类型脑卒中病灶分割：单阶段方法与分层方法的比较。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-04-01 Epub Date: 2024-11-16 DOI: 10.1007/s11517-024-03243-4

Zeynel A Samak

{"title":"Multi-type stroke lesion segmentation: comparison of single-stage and hierarchical approach.","authors":"Zeynel A Samak","doi":"10.1007/s11517-024-03243-4","DOIUrl":"10.1007/s11517-024-03243-4","url":null,"abstract":"Stroke, a major cause of death and disability worldwide, can be haemorrhagic or ischaemic depending on the type of bleeding in the brain. Rapid and accurate identification of stroke type and lesion segmentation is critical for timely and effective treatment. However, existing research primarily focuses on segmenting a single stroke type, potentially limiting their clinical applicability. This study addresses this gap by exploring multi-type stroke lesion segmentation using deep learning methods. Specifically, we investigate two distinct approaches: a single-stage approach that directly segments all tissue types in one model and a hierarchical approach that first classifies stroke types and then utilises specialised segmentation models for each subtype. Recognising the importance of accurate stroke classification for the hierarchical approach, we evaluate ResNet, ResNeXt and ViT networks, incorporating focal loss and oversampling techniques to mitigate the impact of class imbalance. We further explore the performance of U-Net, U-Net++ and DeepLabV3 models for segmentation within each approach. We use a comprehensive dataset of 6650 images provided by the Ministry of Health of the Republic of Türkiye. This dataset includes 1130 ischaemic strokes, 1093 haemorrhagic strokes and 4427 non-stroke cases. In our comparative experiments, we achieve an AUC score of 0.996 when classifying stroke and non-stroke slices. For lesion segmentation task, while the performance of different architectures is comparable, the hierarchical training approach outperforms the single-stage approach in terms of intersection over union (IoU). The performance of the U-Net model increased significantly from an IoU of 0.788 to 0.875 when the hierarchical approach is used. This comparative analysis aims to identify the most effective approach and deep learning model for multi-type stroke lesion segmentation in brain CT scans, potentially leading to improved clinical decision-making, treatment efficiency and outcomes.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"975-986"},"PeriodicalIF":2.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645092","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}

引用次数: 0