IEEE Journal of Biomedical and Health Informatics最新文献_第3页

SGF-RWKV: Semantic-Guided and Frequency-Enhanced RWKV for Unified Medical Image Denoising. SGF-RWKV：用于统一医学图像去噪的语义引导和频率增强RWKV。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-06 DOI: 10.1109/JBHI.2026.3739849

Wenjun Zhang, Xianghong Wang, Yucheng Zeng, Peng Jin, Yue Hu, Tianye Niu

{"title":"SGF-RWKV: Semantic-Guided and Frequency-Enhanced RWKV for Unified Medical Image Denoising.","authors":"Wenjun Zhang, Xianghong Wang, Yucheng Zeng, Peng Jin, Yue Hu, Tianye Niu","doi":"10.1109/JBHI.2026.3739849","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3739849","url":null,"abstract":"Medical image denoising is essential for enhancing diagnostic accuracy and clinical applicability. Since diverse imaging modalities exhibit heterogeneous noise characteristics, developing a unified all-in-one frame work is critical to avoid modality-specific solutions and improve generalization. However, existing approaches struggle with three key challenges: difficulty in uniformly modeling multimodal noise, loss of structural details, and limited spatial context modeling. To addressthis issue, wepropose SGF-RWKV, a unified medical image denoising framework built upon the Receptance Weighted Key Value (RWKV) model for consistent noise modeling across modalities. Specifically, a semantic guided module leverages CLIP based language embeddings to generate task-aware attention maps for adaptive noise suppression, a frequency enhancement module refines structural and edge details via Fourier magnitude modeling, and a dilated multi-scale shift mechanism extends spatial context modeling to preserve continuity and fine texture. By jointly integrating semantic, frequency, and spatial cues, SGF-RWKV achieves modality agnostic denoising within a unified framework. We con ducted comprehensive experiments on five publicly available medical imaging datasets spanning multiple modalities, including MRI, CT, ultrasound, OCT, and cryo-EM. Experimental results demonstrate that our unified denoising framework achieves superior performance across diverse medical imaging modalities, excelling in key quantitative metrics such as PSNR, SSIM, and RMSE. The source code is available at https://github.com/zwj666a/SGF_RWKV.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Contrastive Learning-Based Multi-Atlas Feature Fusion for Brain Disorder Diagnosis. 基于对比学习的多图谱特征融合在脑部疾病诊断中的应用。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-06 DOI: 10.1109/JBHI.2026.3740889

Lina Zhou, Yueying Zhou, Yunling Ma, Lishan Qiao

{"title":"Contrastive Learning-Based Multi-Atlas Feature Fusion for Brain Disorder Diagnosis.","authors":"Lina Zhou, Yueying Zhou, Yunling Ma, Lishan Qiao","doi":"10.1109/JBHI.2026.3740889","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3740889","url":null,"abstract":"Resting-state functional magnetic resonance imaging (rs-fMRI) serves as a widely used non-invasive brain imaging tool for investigating neurological disorders through functional brain networks (FBN) modeling among brain regions of interest (ROIs). Deep learning frameworks, notably graph neural networks and Transformers, have advanced FBN analysis capabilities. However, existing research still faces two major challenges: (1) deep models require large labeled datasets, whereas rs-fMRI data are scarce and heterogeneous, and (2) most existing studies rely on a single brain atlas for FBN, ignoring complementary multi-scale information. To tackle these challenges, we propose MAFCF, a novel Multi-Atlas Feature Contrastive Fusion framework. Specifically, we construct multi-atlas FBN and design an adaptive graph augmentation strategy that perturbs both edges and node features to enhance the robustness of the representations. A hybrid GIN-Transformer encoder is then employed to jointly capture local subgraph patterns and global dependencies, while contrastive loss is used to guide representation learning. Furthermore, semantic preservation and cross-atlas alignment mechanisms are incorporated to ensure the consistency of feature representations. Extensive evaluations on two public datasets indicate that MAFCF consistently outperforms state-of-the-art methods in brain disorder diagnosis.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Efficient Privacy-Preserving Medical Cross-Silo Federated Learning. 高效隐私保护医学跨孤岛联合学习。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-06 DOI: 10.1109/JBHI.2026.3740976

Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss, Philipp Schader, Peter Neher, Santhosh Parampottupadam, Ralf Floca, Klaus Maier-Hein

{"title":"Efficient Privacy-Preserving Medical Cross-Silo Federated Learning.","authors":"Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss, Philipp Schader, Peter Neher, Santhosh Parampottupadam, Ralf Floca, Klaus Maier-Hein","doi":"10.1109/JBHI.2026.3740976","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3740976","url":null,"abstract":"Federated Learning (FL) is becoming increasingly important for advancing machine learning in the medical domain, as it enables collaborative model training across multiple institutions without requiring them to share raw patient data. Unlike traditional centralized machine learning, which gathers data and trains the model in a single location, FL shares model updates among multiple independent participants, thereby keeping their data private. However, this decentralized structure-while privacy-preserving in principle-significantly expands the attack surface. Model updates exchanged between sites introduce new vulnerabilities, increasing the risk of unauthorized access, model poisoning, server-side eavesdropping, and potential leakage of sensitive patient information. Although established homomorphic encryption (HE) schemes such as CKKS, BFV, and BGV offer strong theoretical guarantees for secure computation, their high computational overhead often renders them impractical for real-world medical applications. To address this, we introduce a novel Secure Multi-Party Computation (SMPC)-based protocol tailored specifically for FL in the healthcare context. Our approach holistically rethinks the entire FL-SMPC pipeline, incorporating concurrency at critical stages to improve runtime efficiency while preserving strong security guarantees for the aggregated global model on the server side-an often overlooked aspect in existing SMPC approaches.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Topology-Aware Co-Domain Learning for Ultra-Low-Field MRI Enhancement. 超低场MRI增强的拓扑感知共域学习。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-06 DOI: 10.1109/JBHI.2026.3740158

Sanuwani Dayarathna, Himashi Peiris, Kh Tohidul Islam, Jarrel Seah, Meng Law, Tien-Tsin Wong, Zhaolin Chen

{"title":"Topology-Aware Co-Domain Learning for Ultra-Low-Field MRI Enhancement.","authors":"Sanuwani Dayarathna, Himashi Peiris, Kh Tohidul Islam, Jarrel Seah, Meng Law, Tien-Tsin Wong, Zhaolin Chen","doi":"10.1109/JBHI.2026.3740158","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3740158","url":null,"abstract":"Ultra-low-field (ULF) MRI offers a cost effective and portable imaging alternative for High-Field (HF) MR imaging, but remains limited by its low signal to-noise ratio (SNR) and compressed contrast resolution. Existing enhancement methods have shown promising improvements yet often fail to recover fine anatomical details or over-smooth high-frequency components, limiting their reliability for a high-fidelity synthesis process. To address these challenges, we propose a co-domain diffusion frame work that jointly leverages spatial and frequency representations for ULF enhancement. Our framework integrates two diffusion generators trained collaboratively guided by a topology-aware alignment that preserves the hierarchical organization of brain anatomy. Comprehensive evaluation across multi-contrast imaging demonstrates that our framework achieves comparatively better quantitative and qualitative performance with stronger regional correspondence measured via Dice, RANSAC, and volume correlations. Furthermore, age-volume consistency analysis shows that the synthesized contrasts preserve realistic morphological trends in actual HF images. Evaluation on an external dataset and robustness analysis under controlled input corruption demonstrate reliable performance beyond the training conditions. Moreover, a blinded radiological evaluation demonstrated the clinical reliability of the synthetic results. Overall, the proposed approach yields anatomically coherent and structurally consistent results, offering a robust solution for reliable ULF MRI enhancement.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

MimicPath: Fine-Grained Mitosis Detection and Prognostic Risk Stratification in Rare Tumor Whole-Slide Images. MimicPath：罕见肿瘤全片细粒有丝分裂检测和预后风险分层。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-06 DOI: 10.1109/JBHI.2026.3740982

Fan Zhang, Qing Cai, Cheng Zhang, Shanshan Wang, Ge Ren, Jing Cai, David Zhang, Shuai Han, Tian Li

{"title":"MimicPath: Fine-Grained Mitosis Detection and Prognostic Risk Stratification in Rare Tumor Whole-Slide Images.","authors":"Fan Zhang, Qing Cai, Cheng Zhang, Shanshan Wang, Ge Ren, Jing Cai, David Zhang, Shuai Han, Tian Li","doi":"10.1109/JBHI.2026.3740982","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3740982","url":null,"abstract":"Mitosis detection in whole-slide images provides key histopathological evidence for prognostic risk stratification in gastrointestinal stromal tumor (GIST), a rare, recurrence-prone mesenchymal tumor. Existing pathology foundation models have shown promising performance in common cancer subtyping and tissue classification, but remain suboptimal for reliable fine-grained mitosis detection in rare tumor whole-slide images (WSIs) with extreme target sparsity and pronounced morphological heterogeneity. To address this challenge, we present MimicPath, a novel vision-language framework that mimics the clinical diagnostic workflow by progressively focusing on mitosis-relevant regions in WSIs. It achieves fine-grained mitosis detection and accurate prognostic risk stratification across multi-center GIST WSIs. Specifically, MimicPath differs from existing approaches in three important ways. (1) We introduce a visual-concept prompted focus (VCPF) module that converts learned disease-specific concepts into spatially aware attention priors, effectively localizing sparse mitosis-relevant regions in gigapixel WSIs. (2) We propose a cross-center decoupling (CCD) module that disentangles domain-agnostic features from domain-specific variations via a dual-branch adaptive gating strategy, enhancing generalization across multiple centers. (3) We design a dual-query parsing (DQP) mechanism that integrates structured clinical reports as textual queries to guide multi-stage visual modulation. It enables clinically grounded inspection and interpretation of suspicious regions, improving the discrimination of subtle mitotic patterns for fine-grained analysis. Extensive evaluations on four in-house GIST cohorts and two public mitosis benchmarks show that MimicPath achieves state-of-the-art performance, underscoring the promise of orchestrating collaboration among generalist foundation models, disease-specific models, and clinicians for intelligent pathology.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

TwinFedSeg: Anatomical Twin State-Guided Federated Learning for Multimodal Medical Image Segmentation. 用于多模态医学图像分割的解剖双状态引导联邦学习。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-05 DOI: 10.1109/JBHI.2026.3740406

Haoran Zhou, Yingzhi Qin, Pengfei Mu, Sunil Prajapat, Ling Xu, Guangyao Li

{"title":"TwinFedSeg: Anatomical Twin State-Guided Federated Learning for Multimodal Medical Image Segmentation.","authors":"Haoran Zhou, Yingzhi Qin, Pengfei Mu, Sunil Prajapat, Ling Xu, Guangyao Li","doi":"10.1109/JBHI.2026.3740406","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3740406","url":null,"abstract":"Federated learning enables privacy-preserving collaborative healthcare, while digital twins can represent institution-specific anatomical and modality states. However, federated multimodal medical image segmentation remains challenged by cross-center heterogeneity and missing modalities, while existing methods largely overlook the anatomical and modality complementarity of client updates. To address this problem, we propose TwinFedSeg, a federated learning framework guided by an anatomical twin state for multimodal medical image segmentation. Each client maintains a lightweight state comprising dynamic class prototypes, structural statistics, modality availability, and reliability. These states guide client selection, model aggregation, and global state updating, while the updated global state is fed back through a lightweight feature modulation adapter for cross-center adaptation under missing modalities. Extensive experiments on BraTS 2023-GLi and ISLES 2022 show that TwinFedSeg consistently outperforms representative federated methods, achieving average Dice scores of 80.77% and 72.58%, respectively, with gains of 3.52 and 4.06 percentage points over FedAvg.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Explainable Transformer-Based Retinal Disease Classification. 可解释的变压器视网膜疾病分类。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-05 DOI: 10.1109/JBHI.2026.3740343

Hazrat Bilal, Ayse Keles, Malika Bendechache

{"title":"Explainable Transformer-Based Retinal Disease Classification.","authors":"Hazrat Bilal, Ayse Keles, Malika Bendechache","doi":"10.1109/JBHI.2026.3740343","DOIUrl":"https://doi.org/10.1109/JBHI.2026.3740343","url":null,"abstract":"Retinal diseases are a leading cause of visual impairment globally. Early detection is crucial for effective management, and recent advances in ocular imaging and deep learning have facilitated the development of automated diagnostic systems. Traditional deep learning methods often struggle to effectively capture the overall structural context of the retina and the subtle, localised pathological features required for accurate multi label classification. Moreover, their decision-making processes typically lack transparency, limiting clinical trust and adoption. In this study, we propose an explainable transformer-based fine-tuned model for retinal disease classification that uses a Vision Transformer architecture enhanced with a Multiple Instance Learning (MIL) mechanism. The model is fine-tuned on the multi label retinal disease (MuReD) dataset, enabling it to simultaneously predict multiple co-existing pathologies. Experimental evaluation on the MuReD dataset demonstrates state-of-the-art performance, with the proposed fine-tuned MIL-VT achieving an ML_F1 (multi label F1) score of 0.672, representing an approximately 10 percentage-point increase over recent transformer-based baselines, alongside higher ML_mAP (multi label mAP) and comparable AUC scores. Additionally, we evaluate our model's performance on an independent Eye Disease Image (EDI) dataset, where it maintains better accuracy, achieving a macro-average F1 of approximately 0.92, highlighting its robust generalisation across datasets. An ablation study further validates the effectiveness of the MIL head and selective fine-tuning strategy, showing their substantial contributions to performance gains. To enhance clinical interpretability, we incorporate Grad CAM visualisations that highlight pathological regions associated with each predicted condition. The combination of better classification performance, explainability, and cross-dataset robustness underscores the potential of our proposed framework to improve automated retinal disease screening in diverse clinical settings.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Rethinking Feature Interactions for Medical Image Segmentation: A Unified Hierarchical Aggregation Framework With Boundary Guidance 医学图像分割中特征交互的再思考：一种具有边界引导的统一层次聚合框架。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2026-03-25 DOI: 10.1109/JBHI.2026.3677486

Chunlin Yu;Yinhao Li;Jiaxun Li;Zheng Zhao;Taohong Zhang

{"title":"Rethinking Feature Interactions for Medical Image Segmentation: A Unified Hierarchical Aggregation Framework With Boundary Guidance","authors":"Chunlin Yu;Yinhao Li;Jiaxun Li;Zheng Zhao;Taohong Zhang","doi":"10.1109/JBHI.2026.3677486","DOIUrl":"10.1109/JBHI.2026.3677486","url":null,"abstract":"Medical image segmentation is a crucial task of medical image analysis and computer vision. Medical images, compared to natural ones, contain more complex semantic information, making feature learning more challenging. Existing encoder-decoder architectures are limited by inadequate cross-scale interaction and insufficient boundary modeling in their feature fusion designs. To address this, we propose a Hierarchical Feature Interaction network with Boundary guidance (HFIBNet), which unifies dynamic cross-level feature fusion and explicit edge supervision within a coarse-to-fine segmentation framework. Specifically, we introduce a Boundary Prediction (BP) module to extract boundary-aware features that guide the fusion process. A Cross-Level Feature Fusion (CLFF) module is designed to promote semantic interaction across adjacent encoder stages, while the Edge Feature Aggregation (EFA) module propagates boundary cues hierarchically to enhance structural consistency. Furthermore, a Partially Parallel Decoder (PPD) generates a coarse global prediction, which is progressively refined by a Global-Local Feature Enrichment (GLFE) module, mimicking the clinical annotation workflow from coarse localization to fine delineation. Extensive experiments on ten public medical segmentation datasets across four distinct tasks demonstrate that HFIBNet consistently outperforms existing state-of-the-art methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8824-8835"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Robust R-Peak Detection in Noisy ECGs via Strip-Attention YOLO With Multilead Fusion 基于多导联融合条带关注YOLO的噪声脑电图鲁棒r峰检测。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2025-10-16 DOI: 10.1109/JBHI.2025.3621703

Wang Peng;Peng Tang;Hao Wang;Yuhang Liu;Qiang Li;Peng Zhang

{"title":"Robust R-Peak Detection in Noisy ECGs via Strip-Attention YOLO With Multilead Fusion","authors":"Wang Peng;Peng Tang;Hao Wang;Yuhang Liu;Qiang Li;Peng Zhang","doi":"10.1109/JBHI.2025.3621703","DOIUrl":"10.1109/JBHI.2025.3621703","url":null,"abstract":"The R-peak in electrocardiogram (ECG) signals is a critical physiological marker for the diagnosis of cardiovascular diseases. Although various R-peak detection methods have been proposed, their performance is often hindered by noise, especially in dynamic ECG monitoring. Furthermore, the potential of harnessing complementary information from 12-lead ECG signals has not been fully exploited. To address these challenges, this study conceptualized 12-lead ECG data as two-dimensional images and employed YOLOv5 as the model’s backbone for R-peak detection, effectively transforming a signal segmentation task into an object detection task in images. Specifically, considering the characteristics of consistent R-peak positions across different leads, we proposed a strip attention mechanism to treat horizontal or vertical strips as tokens for computing inter- and intra-strip attention, enhancing the model’s ability to capture R-peak positional information and likelihood. Additionally, a one-dimensional Manhattan distance-based NMS algorithm was used to minimize redundant detection frames, thereby enhancing model performance. The proposed model was rigorously evaluated on two publicly available datasets, INCART and LUDB, under varying noise conditions. On the INCART dataset, the model achieved F1 scores of 99.97%, 99.86%, 99.63%, and 98.00% at noise levels of Original, SNR = 10 dB, SNR = 5 dB, and SNR = 0 dB, respectively. Similarly, on the LUDB dataset, the F1 scores were 99.89%, 100%, 100%, and 99.86% for the corresponding noise levels. Extensive testing across multiple datasets and noise scenarios demonstrated that the proposed model outperformed existing state-of-the-art methods in terms of accuracy, noise robustness, and generalization capability.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8278-8289"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145307894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Video Object Segmentation With Optimal Frame Auto-Selection Based on Prior Knowledge for Midbrain Assessment in Transcranial Ultrasound 基于先验知识的最优帧自动选择的经颅超声中脑评估视频目标分割。

IF 7.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2026-10-01 Epub Date: 2025-09-29 DOI: 10.1109/JBHI.2025.3614160

Xinyi Wang;Sai Kit Lam;Hongyu Kang;Yu Sun;Chao Hou;Shuai Li;Xin Sun;Fangxian Li;Xiao-Ming Wu;Jiannong Cao;Wei Zhang;Yong-Ping Zheng

{"title":"Video Object Segmentation With Optimal Frame Auto-Selection Based on Prior Knowledge for Midbrain Assessment in Transcranial Ultrasound","authors":"Xinyi Wang;Sai Kit Lam;Hongyu Kang;Yu Sun;Chao Hou;Shuai Li;Xin Sun;Fangxian Li;Xiao-Ming Wu;Jiannong Cao;Wei Zhang;Yong-Ping Zheng","doi":"10.1109/JBHI.2025.3614160","DOIUrl":"10.1109/JBHI.2025.3614160","url":null,"abstract":"Transcranial sonography (TCS) provides a non-invasive means of assessing movement disorders such as Parkinson’s disease (PD). However, current TCS-based evaluations rely heavily on manual operation by experienced physicians, making the process time-consuming and physician-dependent. For the first time, we aimed to develop a hybrid pipeline for real-time video object segmentation (VOS) and automatic optimal frame selection. Eighty-three standardized TCS real-time data comprising 1,992 midbrain frames from Beijing Tiantan Hospital were collected. We adopted three state-of-the-art VOS models (STCN, RDE-VOS, and XMEM) and incorporated anatomical priors to guide optimal frame selection. Specifically, we leveraged the anatomical trend of midbrain morphology to estimate the midbrain radius at the optimal frame and selected the frame where the VOS-segmented midbrain best matched this estimate. The XMEM-based pipeline achieved high segmentation performance (Jaccard: 0.85, Boundary Accuracy: 0.95, Dice: 0.92) and optimal frame selection (Distance: 4.87; Jaccard: 0.92), with efficiency (51.05 FPS, 0.56 s/patient, 661.55 MB). Subgroup analyses confirmed robustness across image quality and PD conditions. Assessment of a junior physician’s selection suggests potential to reduce the expertise gap in optimal frame selection. The proposed hybrid pipeline offers an automated tool for midbrain assessment using TCS, which may help reduce physicians’ workload and minimize subjectivity, particularly supporting junior physicians in mitigating the expertise-demanding nature of TCS. This approach may serve as a foundation for more promising TCS-based assessments in the future, contributing to broader adoption of non-invasive ultrasound techniques in PD evaluation.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8315-8326"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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