Medical & Biological Engineering & Computing最新文献

{"title":"Deformable dose prediction network based on hybrid 2D and 3D convolution for nasopharyngeal carcinoma radiotherapy.","authors":"Yanhua Liu, Wang Luo, Xiangchen Li, Min Liu","doi":"10.1007/s11517-024-03231-8","DOIUrl":"10.1007/s11517-024-03231-8","url":null,"abstract":"<p><p>Radiotherapy is recognized as the primary treatment for nasopharyngeal carcinoma (NPC). Rapid and accurate dose prediction is crucial for enhancing the quality and efficiency of radiotherapy planning. However, the current dose prediction model based on 2D architecture cannot effectively learn the spatial information among slices. Although some studies have explored the incorporation of interslice features through 3D architecture, the resolution properties of medical image anisotropy significantly limit the predictive performance. To address the issues, we propose a novel deformable dose prediction network based on hybrid 2D and 3D convolution for NPC radiotherapy. Specifically, the proposed model innovatively incorporates a 2.5D architecture based on hybrid 2D and 3D convolution, and effectively utilizes the directional information within anisotropic resolutions to achieve cross-scale feature extraction. Additionally, deformable convolution is introduced into the model to enhance the receptive field and effectively handle multi-scale spatial transformations. To improve channel correlation and reduce redundant features, we design a Residual Deformable Squeeze-and-Excitation Module. We conduct extensive experiments on an internal dataset, and the results show that the proposed model outperforms other existing methods in most dosimetric criteria. The proposed model has superior dose prediction performance in NPC radiotherapy, and has important clinical significance for assisting physicists to optimize the treatment plan and improve standardization of radiotherapy planning. The source code is available at https://github.com/CDUTJ102/2.5D-Deformable-UNet .</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"733-747"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548607","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":"NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.","authors":"Álvaro García-Barragán, Ahmad Sakor, Maria-Esther Vidal, Ernestina Menasalvas, Juan Cristobal Sanchez Gonzalez, Mariano Provencio, Víctor Robles","doi":"10.1007/s11517-024-03227-4","DOIUrl":"10.1007/s11517-024-03227-4","url":null,"abstract":"<p><p>Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer (NSSC), a hybrid AI framework that integrates neurosymbolic methods with named entity recognition (NER) and entity linking (EL) to transform unstructured clinical notes into structured terms using medical vocabularies, with the Unified Medical Language System (UMLS) as a case study. NSSC was evaluated on a dataset of clinical notes from breast cancer patients, demonstrating significant improvements in the accuracy of both entity recognition and linking compared to state-of-the-art models. Specifically, NSSC achieved a 33% improvement over BioFalcon and a 58% improvement over scispaCy. By combining large language models (LLMs) with symbolic reasoning, NSSC improves the recognition and interoperability of oncologic entities, enabling seamless integration with existing biomedical knowledge. This approach marks a significant advancement in extracting meaningful information from clinical narratives, offering promising applications in cancer research and personalized patient care.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"749-772"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11891111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142562984","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}

{"title":"Structure preservation constraints for unsupervised domain adaptation intracranial vessel segmentation.","authors":"Sizhe Zhao, Qi Sun, Jinzhu Yang, Yuliang Yuan, Yan Huang, Zhiqing Li","doi":"10.1007/s11517-024-03195-9","DOIUrl":"10.1007/s11517-024-03195-9","url":null,"abstract":"<p><p>Unsupervised domain adaptation (UDA) has received interest as a means to alleviate the burden of data annotation. Nevertheless, existing UDA segmentation methods exhibit performance degradation in fine intracranial vessel segmentation tasks due to the problem of structure mismatch in the image synthesis procedure. To improve the image synthesis quality and the segmentation performance, a novel UDA segmentation method with structure preservation approaches, named StruP-Net, is proposed. The StruP-Net employs adversarial learning for image synthesis and utilizes two domain-specific segmentation networks to enhance the semantic consistency between real images and synthesized images. Additionally, two distinct structure preservation approaches, feature-level structure preservation (F-SP) and image-level structure preservation (I-SP), are proposed to alleviate the problem of structure mismatch in the image synthesis procedure. The F-SP, composed of two domain-specific graph convolutional networks (GCN), focuses on providing feature-level constraints to enhance the structural similarity between real images and synthesized images. Meanwhile, the I-SP imposes constraints on structure similarity based on perceptual loss. The cross-modality experimental results from magnetic resonance angiography (MRA) images to computed tomography angiography (CTA) images indicate that StruP-Net achieves better segmentation performance compared with other state-of-the-art methods. Furthermore, high inference efficiency demonstrates the clinical application potential of StruP-Net. The code is available at https://github.com/Mayoiuta/StruP-Net .</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"609-627"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479182","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":"Load-bearing optimization for customized exoskeleton design based on kinematic gait reconstruction.","authors":"Zhengxin Tu, Jinghua Xu, Zhenyu Dong, Shuyou Zhang, Jianrong Tan","doi":"10.1007/s11517-024-03234-5","DOIUrl":"10.1007/s11517-024-03234-5","url":null,"abstract":"<p><p>This paper presents a load-bearing optimization method for customized exoskeleton design based on kinematic gait reconstruction (KGR). For people with acute joint injury, it is no longer probable to obtain the movement gait via computer vision. With this in mind, the 3D reconstruction can be executed from the CT (computed tomography) or MRI (magnetic resonance imaging) of the injured area, in order to generate micro-morphology of the joint occlusion. Innovatively, the disconnected entities can be registered into a whole by surface topography matching with semi-definite computing, further implementing KGR by rebuilding continuous kinematic skeletal flexion postures. To verify the effectiveness of reconstructed kinematic gait, finite element analysis (FEA) is conducted via Hertz contact theory. The lower limb exoskeleton is taken as a verification instance, where rod length ratio and angular rotation range can be set as the design considerations, so as to optimize the load-bearing parameters, which is suitable for individual kinematic gaits. The instance demonstrates that the proposed KGR helps to provide a design paradigm for optimizing load-bearing capacity, on the basis of which the ergonomic customized exoskeleton can be designed from merely medical images, thereby making it more suitable for the large rehabilitation population.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"807-822"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584782","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":"Correction to: Evaluating and enhancing the robustness of vision transformers against adversarial attacks in medical imaging.","authors":"Elif Kanca, Selen Ayas, Elif Baykal Kablan, Murat Ekinci","doi":"10.1007/s11517-024-03240-7","DOIUrl":"10.1007/s11517-024-03240-7","url":null,"abstract":"","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"691"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607258","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":"Mark3D - A semi-automated open-source toolbox for 3D head- surface reconstruction and electrode position registration using a smartphone camera video.","authors":"Suranjita Ganguly, Malaaika Mihir Chhaya, Ankita Jain, Aditya Koppula, Mohan Raghavan, Kousik Sarathy Sridharan","doi":"10.1007/s11517-024-03228-3","DOIUrl":"10.1007/s11517-024-03228-3","url":null,"abstract":"<p><p>Source localization in EEG necessitates co-registering the EEG sensor locations with the subject's MRI, where EEG sensor locations are typically captured using electromagnetic tracking or 3D scanning of the subject's head with EEG cap, using commercially available 3D scanners. Both methods have drawbacks, where, electromagnetic tracking is slow and immobile, while 3D scanners are expensive. Photogrammetry offers a cost-effective alternative but requires multiple photos to sample the head, with good spatial sampling to adequately reconstruct the head surface. Post-reconstruction, the existing tools for electrode position labelling on the 3D head-surface have limited visual feedback and do not easily accommodate customized montages, which are typical in multi-modal measurements. We introduce Mark3D, an open-source, integrated tool for 3D head-surface reconstruction from phone camera video. It eliminates the need for keeping track of spatial sampling during image capture for video-based photogrammetry reconstruction. It also includes blur detection algorithms, a user-friendly interface for electrode and tracking, and integrates with popular toolboxes such as FieldTrip and MNE Python. The accuracy of the proposed method was benchmarked with the head-surface derived from a commercially available handheld 3D scanner Einscan-Pro + (Shining 3D Inc.,) which we treat as the \"ground truth\". We used reconstructed head-surfaces of ground truth (G1) and phone camera video (M₁₀₈₀) to mark the EEG electrode locations in 3D space using a dedicated UI provided in the tool. The electrode locations were then used to form pseudo-specific MRI templates for individual subjects to reconstruct source information. Somatosensory source activations in response to vibrotactile stimuli were estimated and compared between G1 and M₁₀₈₀. The mean positional errors of the EEG electrodes between G1 and M₁₀₈₀ in 3D space were found to be 0.09 ± 0.01 mm across different cortical areas, with temporal and occipital areas registering a relatively higher error than other regions such as frontal, central or parietal areas. The error in source reconstruction was found to be 0.033 ± 0.016 mm and 0.037 ± 0.017 mm in the left and right cortical hemispheres respectively.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"835-847"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607259","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":"Comprehensive validation of a compact laser speckle contrast imaging system for vascular function assessment: from the laboratory to the clinic.","authors":"Meng-Che Hsieh, Chia-Yu Chang, Ching-Han Hsu, Congo Tak Shing Ching, Lun-De Liao","doi":"10.1007/s11517-024-03211-y","DOIUrl":"10.1007/s11517-024-03211-y","url":null,"abstract":"<p><p>Proper organ functioning relies on adequate blood circulation; thus, monitoring blood flow is crucial for early disease diagnosis. Laser speckle contrast imaging (LSCI) is a noninvasive technique that is widely used for measuring superficial blood flow. In this study, we developed a portable LSCI system using an 805-nm near-infrared laser and a monochrome CMOS camera with a 10 × macro zoom lens. The system achieved a high-resolution imaging (1280 × 1024 pixels) with a working distance of 10 to 35 cm. The relative flow velocities were visualized via a spatial speckle contrast analysis algorithm with a 5 × 5 sliding window. In vitro experiments demonstrated the system's ability to image flow velocities in a fluid model, and a linear relationship was observed between the actual flow rate and the relative flow rate obtained by the system. The correlation coefficient (R²) exceeded 0.83 for volumetric flow rates of 0 to 0.2 ml/min when channel widths were greater than 1.2 mm, and R² > 0.94 was obtained for channel widths exceeding 1.6 mm. Comparisons with laser Doppler flowmetry (LDF) revealed a strong positive correlation between the LSCI and LDF results. In vivo experiments captured postocclusive reactive hyperemic responses in rat hind limbs and human palms and feet. The main research contribution is the development of this compact and portable LSCI device, as well as the validation of its reliability and convenience in various scenarios and environments. Future applications of this technology include evaluating blood flow changes during skin injuries, such as abrasions, burns, and diabetic foot ulcers, to aid medical institutions in treatment optimization and to reduce treatment duration.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"641-659"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511894","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":"Classification of breast cancer histopathology images using a modified supervised contrastive learning method.","authors":"Matina Mahdizadeh Sani, Ali Royat, Mahdieh Soleymani Baghshah","doi":"10.1007/s11517-024-03224-7","DOIUrl":"10.1007/s11517-024-03224-7","url":null,"abstract":"<p><p>Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically in classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often succumbing to overfitting by excessively memorizing the limited information available. This work addresses the challenge mentioned above by improving the supervised contrastive learning method leveraging both image-level labels and domain-specific augmentations to enhance model robustness. This approach integrates self-supervised pre-training with a two-stage supervised contrastive learning strategy. In the first stage, we employ a modified supervised contrastive loss that not only focuses on reducing false negatives but also introduces an elimination effect to address false positives. In the second stage, a relaxing mechanism is introduced that refines positive and negative pairs based on similarity, ensuring that only relevant image representations are aligned. We evaluate our method on the BreakHis dataset, which consists of breast cancer histopathology images, and demonstrate an increase in classification accuracy by 1.45% in the image level, compared to the state-of-the-art method. This improvement corresponds to 93.63% absolute accuracy, highlighting the effectiveness of our approach in leveraging properties of data to learn more appropriate representation space. The code implementation of this study is accessible on GitHub https://github.com/matinamehdizadeh/Breast-Cancer-Detection .</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"721-731"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548606","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":"Optimization of three-dimensional esophageal tumor ablation by simultaneous functioning of multiple electrodes.","authors":"Hongying Wang, Jincheng Zou, Shiqing Zhao, Aili Zhang","doi":"10.1007/s11517-024-03230-9","DOIUrl":"10.1007/s11517-024-03230-9","url":null,"abstract":"<p><p>Radiofrequency ablation is a widely accepted minimal-invasive and effective local treatment for tumors. However, its current application in esophageal cancer treatment is limited to targeting thin and superficial lesions, such as Barrett's Esophagus. This study proposes an optimization method using multiple electrodes simultaneously to regulate the temperature field and achieve conformal ablation of tumors. A particle swarm optimization algorithm, coupled with a three-dimensional thermal ablation model, was developed to optimize the status of the functioning electrodes, the optimal voltage (V_opt), and treatment duration (t_tre) for targeted esophageal tumors. This approach takes into account both the electrical and thermal interactions of the electrodes. The results indicate that for esophageal cancers at various stages, with thickness (c) ranging from 4.5 mm to 10.0 mm, major axis (a) ranging from 7.3 mm to 27.3 mm, and minor axis (b) equaling 7.3 mm or 27.3 mm, as well as non-symmetrical geometries, complete tumor coverage (over 99.5%) close to conformal can be achieved. This method illustrates possible precise conformal ablation of esophageal cancers and it may also be used for conformal treatments of other intraluminal lesions.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"793-806"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577143","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":"MFP-YOLO: a multi-scale feature perception network for CT bone metastasis detection.","authors":"Wenrui Lu, Wei Zhang, Yanyan Liu, Lingyun Xu, Yimeng Fan, Zhaowei Meng, Qiang Jia","doi":"10.1007/s11517-024-03221-w","DOIUrl":"10.1007/s11517-024-03221-w","url":null,"abstract":"<p><p>Bone metastasis is one of the most common forms of metastasis in the late stages of malignancy. The early detection of bone metastases can help clinicians develop appropriate treatment plans. CT images are essential for diagnosing and assessing bone metastases in clinical practice. However, early bone metastasis lesions occupy a small part of the image and display variable sizes as the condition progresses, which adds complexity to the detection. To improve diagnostic efficiency, this paper proposes a novel algorithm-MFP-YOLO. Building on the YOLOv5 algorithm, this approach introduces a feature extraction module capable of capturing global information and designs a new content-aware feature pyramid structure to improve the network's capability in processing lesions of varying sizes. Moreover, this paper innovatively applies a transformer-structure decoder to bone metastasis detection. A dataset comprising 3921 CT images was created specifically for this task. The proposed method outperforms the baseline model with a 5.5% increase in precision and a 7.7% boost in recall. The experimental results indicate that this method can meet the needs of bone metastasis detection tasks in real scenarios and provide assistance for medical diagnosis.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"629-640"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479181","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}