Medical & Biological Engineering & Computing最新文献

{"title":"Deformation registration based on reconstruction of brain MRI images with pathologies.","authors":"Li Lian, Qing Chang","doi":"10.1007/s11517-025-03319-9","DOIUrl":"https://doi.org/10.1007/s11517-025-03319-9","url":null,"abstract":"<p><p>Deformable registration between brain tumor images and brain atlas has been an important tool to facilitate pathological analysis. However, registration of images with tumors is challenging due to absent correspondences induced by the tumor. Furthermore, the tumor growth may displace the tissue, causing larger deformations than what is observed in healthy brains. Therefore, we propose a new reconstruction-driven cascade feature warping (RCFW) network for brain tumor images. We first introduce the symmetric-constrained feature reasoning (SFR) module which reconstructs the missed normal appearance within tumor regions, allowing a dense spatial correspondence between the reconstructed quasi-normal appearance and the atlas. The dilated multi-receptive feature fusion module is further introduced, which collects long-range features from different dimensions to facilitate tumor region reconstruction, especially for large tumor cases. Then, the reconstructed tumor images and atlas are jointly fed into the multi-stage feature warping module (MFW) to progressively predict spatial transformations. The method was performed on the Multimodal Brain Tumor Segmentation (BraTS) 2021 challenge database and compared with six existing methods. Experimental results showed that the proposed method effectively handles the problem of brain tumor image registration, which can maintain the smooth deformation of the tumor region while maximizing the image similarity of normal regions.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143383915","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 hardware-efficient on-implant spike compression processor based on VQ-DAE for brain-implantable microsystems.","authors":"Nazanin Ahmadi-Dastgerdi, Hossein Hosseini-Nejad, Hamid Alinejad-Rokny","doi":"10.1007/s11517-025-03317-x","DOIUrl":"https://doi.org/10.1007/s11517-025-03317-x","url":null,"abstract":"<p><p>High-density implantable neural recording microsystems deal with a huge amount of data. Since the wireless transmission of the raw recorded data leads to excessive bandwidth requirements, spike compression approaches have become vital to such systems. The compression processor is designed to be implemented on the implant and so to avoid any tissue damage, the hardware cost of the processor is of great importance. The vector quantization (VQ) algorithm has proven to be effective in compression applications and spike compression systems as well. In this paper, benefiting from the capabilities of the denoising autoencoders (DAE), we propose a solution to enhance the compression performance of the VQ-based approach in terms of both reconstruction accuracy and hardware efficiency. Moreover, we develop a hardware-efficient multi-channel architecture for the proposed VQ-DAE processor. The processor has been implemented in a 180-nm CMOS technology and the validation and verification processes confirm that it provides satisfactory results. It achieves an average signal-to-noise-distortion (SNDR) of 14.51 at a spike compression ratio (SCR) of 30. Operated at a clock frequency of 192 kHz and a supply voltage of 1.8 V, the circuit consumes a power of 4.88 <math><mrow><mi>μ</mi> <mi>W</mi></mrow> </math> and a silicon area of 0.14 mm² per channel.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143374946","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 monocular thoracoscopic 3D scene reconstruction framework based on NeRF.","authors":"Juntao Han, Ziming Zhang, Wenjun Tan, Yufei Wang, Mingxiao Li","doi":"10.1007/s11517-025-03316-y","DOIUrl":"https://doi.org/10.1007/s11517-025-03316-y","url":null,"abstract":"<p><p>With the increasing use of image-based 3D reconstruction in medical procedures, accurate scene reconstruction plays a crucial role in surgical navigation and assisted treatment. However, the monotonous colors, limited image features, and obvious brightness fluctuations of thoracoscopic scenes make the feature point matching process, on which traditional 3D reconstruction methods rely, unstable and unreliable. It brings a great challenge to accurate 3D reconstruction. In this study, a new method for implicit 3D reconstruction of monocular thoracoscopic scenes is proposed. The method combines a pre-trained metric depth estimation model with neural radiation field (NeRF) technique and uses dense SLAM to accurately compute the camera pose. To ensure the accuracy of the depth values and the structural consistency of the reconstructed scene, depth and normal constraints are added to the original color constraints of the NeRF network to achieve high-quality scene reconstruction results. We conducted experiments on the SCARED dataset and the clinical dataset. After comparing with other methods, the depth estimation accuracy and point cloud reconstruction quality of this paper outperform the existing methods. The method in this paper can provide more accurate 3D reconstruction of complex thoracic surgical scenes, which can significantly improve the accuracy and therapeutic efficacy of surgical navigation.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143374947","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":"Patient performance assessment methods for upper extremity rehabilitation in assist-as-needed therapy strategies: a comprehensive review.","authors":"Erkan Ödemiş, Cabbar Veysel Baysal, Mustafa İnci","doi":"10.1007/s11517-025-03315-z","DOIUrl":"https://doi.org/10.1007/s11517-025-03315-z","url":null,"abstract":"<p><p>This paper aims to comprehensively review patient performance assessment (PPA) methods used in assist-as-needed (AAN) robotic therapy for upper extremity rehabilitation. AAN strategies adjust robotic assistance according to the patient's performance, aiming to enhance engagement and recovery in individuals with motor impairments. This review categorizes the implemented PPA methods in the literature for the first time in such a wide scope and suggests future research directions to improve adaptive and personalized therapy. At first, the studies are examined to evaluate PPA methods, which are subsequently categorized according to their underlying implementation strategies: position error-based methods, force-based methods, electromyography (EMG), electroencephalography (EEG)-based methods, performance indicator-based methods, and physiological signal-based methods. The advantages and limitations of each method are discussed. In addition to the classification of PPA methods, the current study also examines clinically tested AAN strategies applied in upper extremity rehabilitation and their clinical outcomes. Clinical findings from these trials demonstrate the potential of AAN strategies in improving motor function and patient engagement. Nevertheless, more extensive clinical testing is necessary to establish the long-term benefits of these strategies over conventional therapies. Ultimately, this review aims to guide future developments in the field of robotic rehabilitation, providing researchers with insights into optimizing AAN strategies for enhanced patient outcomes.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366652","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 novel deep learning framework for retinal disease detection leveraging contextual and local features cues from retinal images.","authors":"Sultan Daud Khan, Saleh Basalamah, Ahmed Lbath","doi":"10.1007/s11517-025-03314-0","DOIUrl":"https://doi.org/10.1007/s11517-025-03314-0","url":null,"abstract":"<p><p>Retinal diseases are a serious global threat to human vision, and early identification is essential for effective prevention and treatment. However, current diagnostic methods rely on manual analysis of fundus images, which heavily depends on the expertise of ophthalmologists. This manual process is time-consuming and labor-intensive and can sometimes lead to missed diagnoses. With advancements in computer vision technology, several automated models have been proposed to improve diagnostic accuracy for retinal diseases and medical imaging in general. However, these methods face challenges in accurately detecting specific diseases within images due to inherent issues associated with fundus images, including inter-class similarities, intra-class variations, limited local information, insufficient contextual understanding, and class imbalances within datasets. To address these challenges, we propose a novel deep learning framework for accurate retinal disease classification. This framework is designed to achieve high accuracy in identifying various retinal diseases while overcoming inherent challenges associated with fundus images. Generally, the framework consists of three main modules. The first module is Densely Connected Multidilated Convolution Neural Network (DCM-CNN) that extracts global contextual information by effectively integrating novel Casual Dilated Dense Convolutional Blocks (CDDCBs). The second module of the framework, namely, Local-Patch-based Convolution Neural Network (LP-CNN), utilizes Class Activation Map (CAM) (obtained from DCM-CNN) to extract local and fine-grained information. To identify the correct class and minimize the error, a synergic network is utilized that takes the feature maps of both DCM-CNN and LP-CNN and connects both maps in a fully connected fashion to identify the correct class and minimize the errors. The framework is evaluated through a comprehensive set of experiments, both quantitatively and qualitatively, using two publicly available benchmark datasets: RFMiD and ODIR-5K. Our experimental results demonstrate the effectiveness of the proposed framework and achieves higher performance on RFMiD and ODIR-5K datasets compared to reference methods.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366651","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":"Transformer-based fusion model for mild depression recognition with EEG and pupil area signals.","authors":"Jing Zhu, Yuanlong Li, Changlin Yang, Hanshu Cai, Xiaowei Li, Bin Hu","doi":"10.1007/s11517-024-03269-8","DOIUrl":"https://doi.org/10.1007/s11517-024-03269-8","url":null,"abstract":"<p><p>Early detection and treatment are crucial for the prevention and treatment of depression; compared with major depression, current researches pay less attention to mild depression. Meanwhile, analysis of multimodal biosignals such as EEG, eye movement data, and magnetic resonance imaging provides reliable technical means for the quantitative analysis of depression. However, how to effectively capture relevant and complementary information between multimodal data so as to achieve efficient and accurate depression recognition remains a challenge. This paper proposes a novel Transformer-based fusion model using EEG and pupil area signals for mild depression recognition. We first introduce CSP into the Transformer to construct single-modal models of EEG and pupil data and then utilize attention bottleneck to construct a mid-fusion model to facilitate information exchange between the two modalities; this strategy enables the model to learn the most relevant and complementary information for each modality and only share the necessary information, which improves the model accuracy while reducing the computational cost. Experimental results show that the accuracy of the EEG and pupil area signals of single-modal models we constructed is 89.75% and 84.17%, the precision is 92.04% and 95.21%, the recall is 89.5% and 71%, the specificity is 90% and 97.33%, the F1 score is 89.41% and 78.44%, respectively, and the accuracy of mid-fusion model can reach 93.25%. Our study demonstrates that the Transformer model can learn the long-term time-dependent relationship between EEG and pupil area signals, providing an idea for designing a reliable multimodal fusion model for mild depression recognition based on EEG and pupil area signals.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257180","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":"Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution.","authors":"Renjun Wen, Yufei Ma, Changdong Liu, Renwei Feng","doi":"10.1007/s11517-025-03291-4","DOIUrl":"https://doi.org/10.1007/s11517-025-03291-4","url":null,"abstract":"<p><p>Breast cancer affects a significant number of patients worldwide, and early diagnosis is critical for improving cure rates and prognosis. Deep learning-based breast cancer classification algorithms have substantially alleviated the burden on medical personnel. However, existing breast cancer diagnosis models face notable limitations which are challenging to obtain in clinical settings, such as reliance on a large volume of labeled samples, an inability to comprehensively extract features from breast cancer images, and susceptibility to overfitting on account of imbalanced class distribution. Therefore, we propose the class-aware multi-level attention learning model focused on semi-supervised breast cancer diagnosis to effectively reduce the dependency on extensive data annotation. Additionally, we develop the multi-level fusion attention learning module, which integrates multiple mutual attention components across different layers, allowing the model to precisely identify critical regions for lesion categorization. Finally, we design the class-aware adaptive pseudo-labeling module which adaptively predicts category distribution in unlabeled data, and directs the model to focus on underrepresented categories, ensuring a balanced learning process. Experimental results on the BACH dataset demonstrate that our proposed model achieves an accuracy of 86.7% with only 40% labeled microscopic data, showcasing its outstanding contribution to semi-supervised breast cancer diagnosis.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191137","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":"Breast cancer image classification based on H&E staining using a causal attention graph neural network model.","authors":"Xiaoya Chang, Zhongrong Zhang, Jianguo Sun, Kang Lin, Ping'an Song","doi":"10.1007/s11517-025-03303-3","DOIUrl":"https://doi.org/10.1007/s11517-025-03303-3","url":null,"abstract":"<p><p>Breast cancer image classification remains a challenging task due to the high-resolution nature of pathological images and their complex feature distributions. Graph neural networks (GNNs) offer promising capabilities to capture local structural information but often suffer from limited generalization and reliance on shortcut features. This study proposes a novel causal discovery attention-based graph neural network (CDA-GNN) model. The model converts high-resolution image data into graph data using superpixel segmentation and employs a causal attention mechanism to identify and utilize key causal features. A backdoor adjustment strategy further disentangles causal features from shortcut features, enhancing model interpretability and robustness. Experimental evaluations on the 2018 BACH breast cancer image dataset demonstrate that CDA-GNN achieves a classification accuracy of 86.36%. Additional metrics, including F1-score and ROC, validate the superior performance and generalization of the proposed approach. The CDA-GNN model, with its powerful automated cancer image analysis capabilities and strong interpretability, provides an effective tool for clinical applications. It significantly reduces the workload of healthcare professionals while facilitating the early detection and diagnosis of breast cancer, thereby improving diagnostic efficiency and accuracy.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191132","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":"Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech.","authors":"Yuchuan Liu, Lianzhi Li, Yu Rao, Huihua Cao, Xiaoheng Tan, Yongsong Li","doi":"10.1007/s11517-025-03299-w","DOIUrl":"https://doi.org/10.1007/s11517-025-03299-w","url":null,"abstract":"<p><p>Diagnosing Parkinson's disease (PD) via speech is crucial for its non-invasive and convenient data collection. However, the small sample size of PD speech data impedes accurate recognition of PD speech. Therefore, we propose a novel multi-source sparse broad transfer learning (SBTL) method, inspired by incremental broad learning, which balances model learning capability and the overfitting associated with limited sample size of PD speech data. Specifically, SBTL initially leverages a sparse network to preprocess highly overlapping PD speech data, facilitating the identification of intrinsic invariant features between the multi-source auxiliary domain and the target data, which contributes to reducing model complexity. Subsequently, SBTL evaluate transfer effectiveness by virtue of the incremental learning mechanism, adaptively adjusting model structure to ensure the positive transfer of knowledge from the multi-source auxiliary domains to the target domain. Numerous experimental results show that, compared to transfer learning methods for PD diagnosis via speech, SBTL consistently demonstrates significant advantages with a smaller standard deviation, particularly leading by at least 2.58%, 5.71%, 12%, and 14.81% in accuracy, precision, sensitivity, and F1-score, respectively. Even when compared to some well-known transfer learning methods, SBTL still exhibits significant advantages in most cases while maintaining comparable sensitivity. These demonstrate that SBTL is an effective, efficient, and stable multi-source transfer learning method for PD speech recognition, giving more accurate assistance information for clinicians on decision-making for PD in practice.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191155","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":"Finite element stress analysis of the hindfoot after medial displacement calcaneal osteotomy with different translation distances.","authors":"Jinyang Lyu, Jian Xu, Jiazhang Huang, Chao Zhang, Xu Wang, Jian Yu, Xin Ma","doi":"10.1007/s11517-025-03309-x","DOIUrl":"https://doi.org/10.1007/s11517-025-03309-x","url":null,"abstract":"<p><p>The medial displacement calcaneal osteotomy (MDCO) is one of commonly used procedures to restore the hindfoot alignment of the flatfoot deformity. However, the selection of the amount of translation for MDCO and its biomechanical effect on the hindfoot was rarely reported. This study employs finite element analysis to investigate stress distribution in the hindfoot following MDCO across varying translation distances. An adult-acquired flatfoot deformity (AAFD) finite element (FE) model consisting of 16 bones, 56 ligaments, and soft tissues was used. MDCO procedure was simulated with the translation distance of 0 mm, 2 mm, 4 mm, 6 mm, 8 mm, 10 mm, 12 mm, and 14 mm. Contact pressure on the plantar surface, the articular surface of the tibiotalar joint and the subtalar joint, and von Mises stress on the resection surface of the calcaneus under different translation distances were analyzed and compared. Results showed the MDCO reduces 12.46 to 33.32% peak contact pressure on the plantar surface, the tibiotalar joint, and the posterior facet of the subtalar joint, and shifts pressure from lateral to medial. But the difference in peak pressure for different translation distances larger than 4 mm was small. The MDCO also reduces the stress on the distal calcaneal resected surface. The study highlights the use of patient-specific computational modeling for preoperative plans.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081553","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}