生物医学工程学杂志最新文献_第9页

[Shape-aware cross-modal domain adaptive segmentation model]. [形状感知跨模态域自适应分割模型]。

生物医学工程学杂志 Pub Date : 2025-12-25 DOI: 10.7507/1001-5515.202506045

Yusi Liu, Liangce Qi, Zhaoheng Diao, Guanyuan Feng, Yuqin Li, Zhengang Jiang

{"title":"[Shape-aware cross-modal domain adaptive segmentation model].","authors":"Yusi Liu, Liangce Qi, Zhaoheng Diao, Guanyuan Feng, Yuqin Li, Zhengang Jiang","doi":"10.7507/1001-5515.202506045","DOIUrl":"10.7507/1001-5515.202506045","url":null,"abstract":"Cross-modal unsupervised domain adaptation (UDA) aims to transfer segmentation models trained on a labeled source modality to an unlabeled target modality. However, existing methods often fail to fully exploit shape priors and intermediate feature representations, resulting in limited generalization ability of the model in cross-modal transfer tasks. To address this challenge, we propose a segmentation model based on shape-aware adaptive weighting (SAWS) that enhance the model's ability to perceive the target area and capture global and local information. Specifically, we design a multi-angle strip-shaped shape perception (MSSP) module that captures shape features from multiple orientations through an angular pooling strategy, improving structural modeling under cross-modal settings. In addition, an adaptive weighted hierarchical contrastive (AWHC) loss is introduced to fully leverage intermediate features and enhance segmentation accuracy for small target structures. The proposed method is evaluated on the multi-modality whole heart segmentation (MMWHS) dataset. Experimental results demonstrate that SAWS achieves superior performance in cross-modal cardiac segmentation tasks, with a Dice score (Dice) of 70.1% and an average symmetric surface distance (ASSD) of 4.0 for the computed tomography (CT)→magnetic resonance imaging (MRI) task, and a Dice of 83.8% and ASSD of 3.7 for the MRI→CT task, outperforming existing state-of-the-art methods. Overall, this study proposes a cross-modal medical image segmentation method with shape-aware, which effectively improves the structure-aware ability and generalization performance of the UDA model.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 6","pages":"1216-1225"},"PeriodicalIF":0.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12744971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145834932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Predicting epileptic seizures based on a multi-convolution fusion network]. [基于多卷积融合网络预测癫痫发作]。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202502059

Xueting Shen, Yan Piao, Huiru Yang, Haitong Zhao

{"title":"[Predicting epileptic seizures based on a multi-convolution fusion network].","authors":"Xueting Shen, Yan Piao, Huiru Yang, Haitong Zhao","doi":"10.7507/1001-5515.202502059","DOIUrl":"10.7507/1001-5515.202502059","url":null,"abstract":"Current epilepsy prediction methods are not effective in characterizing the multi-domain features of complex long-term electroencephalogram (EEG) data, leading to suboptimal prediction performance. Therefore, this paper proposes a novel multi-scale sparse adaptive convolutional network based on multi-head attention mechanism (MS-SACN-MM) model to effectively characterize the multi-domain features. The model first preprocesses the EEG data, constructs multiple convolutional layers to effectively avoid information overload, and uses a multi-layer perceptron and multi-head attention mechanism to focus the network on critical pre-seizure features. Then, it adopts a focal loss training strategy to alleviate class imbalance and enhance the model's robustness. Experimental results show that on the publicly created dataset (CHB-MIT) by MIT and Boston Children's Hospital, the MS-SACN-MM model achieves a maximum accuracy of 0.999 for seizure prediction 10 ~ 15 minutes in advance. This demonstrates good predictive performance and holds significant importance for early intervention and intelligent clinical management of epilepsy patients.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"987-993"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges]. [人工智能在预测乳腺癌新辅助化疗病理完全缓解中的应用：当前进展和挑战]。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202503075

Sunwei He, Xiujuan Li, Yuanzhong Xie, Jixue Hou, Baosan Han, Shengdong Nie

{"title":"[Artificial intelligence in predicting pathological complete response to neoadjuvant chemotherapy for breast cancer: current advances and challenges].","authors":"Sunwei He, Xiujuan Li, Yuanzhong Xie, Jixue Hou, Baosan Han, Shengdong Nie","doi":"10.7507/1001-5515.202503075","DOIUrl":"10.7507/1001-5515.202503075","url":null,"abstract":"With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"1076-1084"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Application of nanomaterials-enhanced magnetic resonance imaging in precise diagnosis of pan-vascular diseases]. 【纳米材料增强磁共振成像在泛血管疾病精准诊断中的应用】。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202412068

Yao Li, Peisen Zhang, Ni Zhang

{"title":"[Application of nanomaterials-enhanced magnetic resonance imaging in precise diagnosis of pan-vascular diseases].","authors":"Yao Li, Peisen Zhang, Ni Zhang","doi":"10.7507/1001-5515.202412068","DOIUrl":"10.7507/1001-5515.202412068","url":null,"abstract":"Pan-vascular diseases encompass a range of systemic conditions characterized by sharing a common pathologic basis of vascular deterioration. Due to the complexity of these diseases, a thorough understanding on their similarities and differences is essential for optimizing diagnosis and treatment strategies. Magnetic resonance imaging (MRI), as one of the commonly used medical imaging techniques, has been widely applied in the diagnosis of pan-vascular diseases. Particularly, the integration of MRI with contrast agents and multi-parameter imaging techniques significantly enhances diagnostic accuracy, reducing the likelihood of missed or incorrect diagnoses. Recently, a variety of nano-magnetic resonance contrast agents have been developed and applied to the magnetic resonance imaging diagnosis of diseases. These nanotechnology-based contrast agents provide multiple advantages, ensuring more precise and forward-looking imaging of pan-vascular conditions. In this review, the diverse application strategies of nanomaterials-enhanced MRI techniques in the diagnosis of pan-vascular diseases were systematically summarized, by classifying them into the commonly used MRI sequences in clinical practice. Additionally, the potential advantages and challenges associated with the clinical translation of nanomaterial-enhanced MRI were also discussed. This review not only offers a novel perspective on the precise diagnosis of pan-vascular diseases, but also serves as a valuable reference for future clinical practice and research in the field.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"1092-1098"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Advances in radiomics for early diagnosis and precision treatment of lung cancer]. [放射组学在肺癌早期诊断和精准治疗中的研究进展]。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202405059

Jiayi Li, Wenxin Luo, Zhoufeng Wang, Weimin Li

引用次数: 0

[A head direction cell model based on a spiking neural network with landmark-free calibration]. [基于无地标校准的尖峰神经网络的头部方向细胞模型]。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202503025

Naigong Yu, Jingsen Huang, Ke Lin, Zhiwen Zhang

{"title":"[A head direction cell model based on a spiking neural network with landmark-free calibration].","authors":"Naigong Yu, Jingsen Huang, Ke Lin, Zhiwen Zhang","doi":"10.7507/1001-5515.202503025","DOIUrl":"10.7507/1001-5515.202503025","url":null,"abstract":"In animal navigation, head direction is encoded by head direction cells within the olfactory-hippocampal structures of the brain. Even in darkness or unfamiliar environments, animals can estimate their head direction by integrating self-motion cues, though this process accumulates errors over time and undermines navigational accuracy. Traditional strategies rely on visual input to correct head direction, but visual scenes combined with self-motion information offer only partially accurate estimates. This study proposed an innovative calibration mechanism that dynamically adjusts the association between visual scenes and head direction based on the historical firing rates of head direction cells, without relying on specific landmarks. It also introduced a method to fine-tune error correction by modulating the strength of self-motion input to control the movement speed of the head direction cell activity bump. Experimental results showed that this approach effectively reduced the accumulation of self-motion-related errors and significantly enhanced the accuracy and robustness of the navigation system. These findings offer a new perspective for biologically inspired robotic navigation systems and underscore the potential of neural mechanisms in enabling efficient and reliable autonomous navigation.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"970-976"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Ethical considerations for artificial intelligence-enhanced brain-computer interface]. [人工智能增强脑机接口的伦理考虑]。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202507024

Yuyu Cao, Yuhang Xue, Hengyuan Yang, Fan Wang, Tianwen Li, Lei Zhao, Yunfa Fu

{"title":"[Ethical considerations for artificial intelligence-enhanced brain-computer interface].","authors":"Yuyu Cao, Yuhang Xue, Hengyuan Yang, Fan Wang, Tianwen Li, Lei Zhao, Yunfa Fu","doi":"10.7507/1001-5515.202507024","DOIUrl":"10.7507/1001-5515.202507024","url":null,"abstract":"Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users' intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"1085-1091"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Deep overparameterized blood cell detection algorithm utilizing hybrid attention mechanisms]. [利用混合注意机制的深度超参数化血细胞检测算法]。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202412057

Shuo Zhu, Xukang Zhang, Zongyang Wang, Rui Jiang, Zhengda Liu

{"title":"[Deep overparameterized blood cell detection algorithm utilizing hybrid attention mechanisms].","authors":"Shuo Zhu, Xukang Zhang, Zongyang Wang, Rui Jiang, Zhengda Liu","doi":"10.7507/1001-5515.202412057","DOIUrl":"10.7507/1001-5515.202412057","url":null,"abstract":"To address the challenges in blood cell recognition caused by diverse morphology, dense distribution, and the abundance of small target information, this paper proposes a blood cell detection algorithm - the \"You Only Look Once\" model based on hybrid mixing attention and deep over-parameters (HADO-YOLO). First, a hybrid attention mechanism is introduced into the backbone network to enhance the model's sensitivity to detailed features. Second, the standard convolution layers with downsampling in the neck network are replaced with deep over-parameterized convolutions to expand the receptive field and improve feature representation. Finally, the detection head is decoupled to enhance the model's robustness for detecting abnormal cells. Experimental results on the Blood Cell Counting Dataset (BCCD) demonstrate that the HADO-YOLO algorithm achieves a mean average precision of 90.2% and a precision of 93.8%, outperforming the baseline YOLO model. Compared with existing blood cell detection methods, the proposed algorithm achieves state-of-the-art detection performance. In conclusion, HADO-YOLO offers a more efficient and accurate solution for identifying various types of blood cells, providing valuable technical support for future clinical diagnostic applications.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"936-944"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Structural design and mechanical analysis of a "drum-shaped" balloon-expandable valve stent in expanded configuration]. 一种“鼓形”球囊膨胀式瓣膜支架的结构设计与力学分析。

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202505020

Youzhi Zhao, Qianwen Hou, Jianye Zhou, Shiliang Chen, Hanbing Zhang, Aike Qiao

{"title":"[Structural design and mechanical analysis of a \"drum-shaped\" balloon-expandable valve stent in expanded configuration].","authors":"Youzhi Zhao, Qianwen Hou, Jianye Zhou, Shiliang Chen, Hanbing Zhang, Aike Qiao","doi":"10.7507/1001-5515.202505020","DOIUrl":"10.7507/1001-5515.202505020","url":null,"abstract":"Stent migration is one of the common complications following transcatheter valve implantation. This study aims to design a \"drum-shaped\" balloon-expandable aortic valve stent to address this issue and conduct a mechanical analysis. The implantation process of the stent was evaluated using a method that combines numerical simulation and in vitro experiments. Furthermore, the fatigue process of the stent under pulsatile cyclic loading was simulated, and its fatigue performance was assessed using a Goodman diagram. The process of the stent migrating toward the left ventricular side was simulated, and the force-displacement curve of the stent was extracted to evaluate its anti- migration performance. The results showed that all five stent models could be crimped into a 14F sheath and enabled uniform expansion of the native valve leaflets. The stress in each stent was below the ultimate stress, so no fatigue fracture occurred. As the cell height ratio decreased, the contact area fraction between the stent and the aortic root gradually decreased. However, the mean contact force and the maximum anti-migration force first decreased and then increased. Specifically, model S5 had the smallest contact area fraction but the largest mean contact force and maximum anti-migration force, reaching approximately 0.16 MPa and 10.73 N, respectively. The designed stent achieves a \"drum-shaped\" change after expansion and has good anti-migration performance.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"945-953"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A multi-scale feature capturing and spatial position attention model for colorectal polyp image segmentation]. 基于多尺度特征捕获和空间位置关注模型的结直肠息肉图像分割

生物医学工程学杂志 Pub Date : 2025-10-25 DOI: 10.7507/1001-5515.202412012

Wen Guo, Xiangyang Chen, Jian Wu, Jiaqi Li, Pengxue Zhu

{"title":"[A multi-scale feature capturing and spatial position attention model for colorectal polyp image segmentation].","authors":"Wen Guo, Xiangyang Chen, Jian Wu, Jiaqi Li, Pengxue Zhu","doi":"10.7507/1001-5515.202412012","DOIUrl":"10.7507/1001-5515.202412012","url":null,"abstract":"Colorectal polyps are important early markers of colorectal cancer, and their early detection is crucial for cancer prevention. Although existing polyp segmentation models have achieved certain results, they still face challenges such as diverse polyp morphology, blurred boundaries, and insufficient feature extraction. To address these issues, this study proposes a parallel coordinate fusion network (PCFNet), aiming to improve the accuracy and robustness of polyp segmentation. PCFNet integrates parallel convolutional modules and a coordinate attention mechanism, enabling the preservation of global feature information while precisely capturing detailed features, thereby effectively segmenting polyps with complex boundaries. Experimental results on Kvasir-SEG and CVC-ClinicDB demonstrate the outstanding performance of PCFNet across multiple metrics. Specifically, on the Kvasir-SEG dataset, PCFNet achieved an F1-score of 0.897 4 and a mean intersection over union (mIoU) of 0.835 8; on the CVC-ClinicDB dataset, it attained an F1-score of 0.939 8 and an mIoU of 0.892 3. Compared with other methods, PCFNet shows significant improvements across all performance metrics, particularly in multi-scale feature fusion and spatial information capture, demonstrating its innovativeness. The proposed method provides a more reliable AI-assisted diagnostic tool for early colorectal cancer screening.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 5","pages":"910-918"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12568743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145393584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0