Biomedical Signal Processing and Control最新文献_第8页

Ensemble machine learning and tree-structured Parzen estimator to predict early-stage pancreatic cancer 集成机器学习和树结构Parzen估计预测早期胰腺癌

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-16 DOI: 10.1016/j.bspc.2025.107867

Kah Keng Wong

{"title":"Ensemble machine learning and tree-structured Parzen estimator to predict early-stage pancreatic cancer","authors":"Kah Keng Wong","doi":"10.1016/j.bspc.2025.107867","DOIUrl":"10.1016/j.bspc.2025.107867","url":null,"abstract":"<div><div>Pancreatic ductal adenocarcinoma (PDAC) is one of the most deadly malignancies due to challenges in diagnosing the disease at an early-stage. In this study, the RNA-sequencing data of tumor-educated platelets (GSE183635) were analyzed using machine learning (ML) algorithms to predict early-stage PDAC. Differentially expressed genes specific to early-stage PDAC were selected as features to build the ML models. The ML algorithms used were linear (logistic regression), and non-linear [support vector machine (SVM), random forest (RF), XGBoost (XGB), and LightGBM (GBM)] algorithms. Given the limitations of existing, non-probabilistic algorithms to optimize early-stage PDAC detection, tree-structured Parzen estimator (TPE) algorithm was utilized for hyperparameters optimization through probabilistic modeling. TPE identified the most optimal model for each ML algorithm, particularly effective for optimizing non-linear ML algorithms (SVM, RF, XGB, and GBM). To leverage the strengths of individual ML algorithms, ensemble modeling that combined up to a maximum of three individual algorithms demonstrated that a weighted ensemble integrating SVM, RF, and GBM (<em>i.e.</em>, SVM:RF:GBM ensemble model) outperformed individual models. The SVM:RF:GBM ensemble model showed optimal performance metrics in the calibrated test set (ROC AUC: 0.905; sensitivity: 0.857; specificity: 0.850). In both the calibrated training and test sets, the ensemble model demonstrated consistent performance as measured by 13 different performance metrics, and such consistency was not observed in individual models. In conclusion, the SVM:RF:GBM ensemble model optimized by TPE represents a novel predictive model for early-stage PDAC, and this study proposes a framework for predictive model construction in cancer diagnosis.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107867"},"PeriodicalIF":4.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834658","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

Detection of event-related potentials as a biomarker in major depressive disorder using an XGBoost model 使用XGBoost模型检测事件相关电位作为重性抑郁症的生物标志物

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-16 DOI: 10.1016/j.bspc.2025.107879

Yuhang Pan, Jing Jie, Ming Yin

{"title":"Detection of event-related potentials as a biomarker in major depressive disorder using an XGBoost model","authors":"Yuhang Pan, Jing Jie, Ming Yin","doi":"10.1016/j.bspc.2025.107879","DOIUrl":"10.1016/j.bspc.2025.107879","url":null,"abstract":"<div><h3>Objective:</h3><div>To evaluate biomarkers that assist in the identification of electroencephalography signals in patients with depression, utilizing an ensemble learning model.</div></div><div><h3>Methods:</h3><div>XGBoost ensemble model was trained and tested to classify patients with major depressive disorder (MDD) (n=24) and healthy controls (HCs) (n=29) from the multi-modal open dataset for mental-disorder analysis. Based on event-related potentials (ERPs) to emotional-neutral face pairs (Happy-Neutral, Sad-Neutral, Fear-Neutral) as stimuli, we segmented six conditions: 3 emotional cues, dots (happy, sad, and fear) and applied the FisherScore feature selection method to select the waveform features with high mutual information. Overall, 80% of the data was selected to establish the XGBoost model with five-fold cross-validation.</div></div><div><h3>Results:</h3><div>We identified happy, sad and fear conditions with waveform features (170–230 s) to distinguish patients with depression. The proposed XGBoost model had a comprehensive accuracy, precision, recall, F1-score, and area under curve of 99.52%, 99.39%, 99.67%, 99.52%, and 99.98% for the ERPs. Furthermore, our experimental results indicated that suppression of the amplitude of negative emotional cues could be used to recognize depression, which was predominantly over the frontal lobe and frontal poles regions. The response latency of ERP signals contributed significantly to distinguishing between HCs and patients with MDD.</div></div><div><h3>Conclusion:</h3><div>An ensemble learning system for classification using the XGBoost and feature selection using FisherScore has the potential to be used in clinical prediction of depressive symptoms in patients with MDD.</div></div><div><h3>Significance:</h3><div>The discovery of ERPs as a biomarker has important clinical implications for exploring the pathogenesis behind MDD.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107879"},"PeriodicalIF":4.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834659","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

A framework for segmentation of filarial worm in thick blood smear images using image processing techniques and machine learning algorithms 一种基于图像处理技术和机器学习算法的厚血涂片中丝虫病的分割框架

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-13 DOI: 10.1016/j.bspc.2025.107881

B. Sharmila , K. Kamalanand , R.L.J. De Britto

{"title":"A framework for segmentation of filarial worm in thick blood smear images using image processing techniques and machine learning algorithms","authors":"B. Sharmila , K. Kamalanand , R.L.J. De Britto","doi":"10.1016/j.bspc.2025.107881","DOIUrl":"10.1016/j.bspc.2025.107881","url":null,"abstract":"<div><div>Lymphatic filariasis (LF), also referred to as elephantiasis, is an infectious disease prevalent in tropical regions, caused by parasitic filarial worms and spread through the bites of mosquitoes. Individuals with LF have difficulty in doing routine tasks, resulting in long-term socioeconomic consequences. Hence, early detection and diagnosis are needed to control the infection. Considering the setbacks during COVID-19 pandemic, the Global Program for Elimination of Lymphatic Filariasis (GPELF) revised the elimination target at 2030. Computer-aided detection and segmentation of microfilariae in microscopic blood smear images is expected to detect the microfilaria more preciously compared to routine microscopic examination, in particular, the weakly-stained smears or coiled microfilaria. In this work, the acquired blood smear images were preprocessed with illumination correction, various filtering, and thresholding methods. It was found that, the 2D Jerman Filter and Renyi entropy-based thresholding resulted in best image quality metrics. Further, five different segmentation algorithms were utilized to segment the filarial worm from the images. It was found that the similarity indices between the ground truth and the images segmented using the firefly algorithm were high with an average Dice, Jaccard, and structural similarity index of 0.9816, 0.9779, and 0.9932, respectively. It is observed that the proposed framework accurately segments the worm without losing its proximal and distal portions, despite the presence of artifacts, and variation in shape and size of the worms due to folding or coiling. This work has significant public health impact since automated segmentation of filarial worms is highly desirable for mass screening of lymphatic filariasis particularly during pre-elimination phase and in low- endemic situation.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107881"},"PeriodicalIF":4.9,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823901","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

Advanced ensemble learning-based CNN-BiLSTM network for cardiovascular disease classification using ECG and PCG signal 基于集成学习的CNN-BiLSTM神经网络用于心电和PCG信号的心血管疾病分类

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-12 DOI: 10.1016/j.bspc.2025.107846

Ehsanhosein Kalatehjari , Mohammad Mehdi Hosseini , Ali Harimi , Vahid Abolghasemi

{"title":"Advanced ensemble learning-based CNN-BiLSTM network for cardiovascular disease classification using ECG and PCG signal","authors":"Ehsanhosein Kalatehjari , Mohammad Mehdi Hosseini , Ali Harimi , Vahid Abolghasemi","doi":"10.1016/j.bspc.2025.107846","DOIUrl":"10.1016/j.bspc.2025.107846","url":null,"abstract":"<div><div>Cardiovascular disease (CVD) is a well-known leading cause of death worldwide. This highlights the need for an effective and efficient diagnostic-therapeutic path for the diagnosis and risk stratification of coronary artery disease (CAD) patients. However, it is inaccurate to investigate CAD only based on either electrocardiogram (ECG) or phonocardiogram (PCG) recordings. Several studies have attempted to use a combination of both signals in the early prediction and diagnosis of CAD. Considering the strong capability of deep learning models in feature extraction this research explores the efficiency of a hybrid CNN-BiLSTM ensemble approach that combines ECG and PCG signals to determine cardiac health status. Inspired by the significant performance of ensemble learning techniques in combining multiple base models to enhance overall prediction accuracy, a hybrid network architecture is suggested. The proposed CNN-BiLSTM model is considered a baseline for ECG and PCG signal prediction. Then, a bilinear layer combines both predictions of individual models to obtain a final accurate and robust prediction. It applies a bilinear transformation to incoming outputs from two base models to make the final output. The proposed architecture shows considerable improvement in prediction accuracy compared to using both ECG and PCG signals separately. Employing the well-known PhysioNet/Computing in Cardiology (CinC) Challenge 2016 Database, the proposed method has achieved 97% diagnosis accuracy, which how improvement over comparable methods and various other existing techniques.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107846"},"PeriodicalIF":4.9,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823902","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

Deep learning based retinal disease classification using an autoencoder and generative adversarial network 基于深度学习的视网膜疾病分类，使用自编码器和生成对抗网络

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-11 DOI: 10.1016/j.bspc.2025.107852

G. Jeyasri , R. Karthiyayini

{"title":"Deep learning based retinal disease classification using an autoencoder and generative adversarial network","authors":"G. Jeyasri , R. Karthiyayini","doi":"10.1016/j.bspc.2025.107852","DOIUrl":"10.1016/j.bspc.2025.107852","url":null,"abstract":"<div><div>Human eyesight relies heavily on retinal tissue, vision loss include infections of the retina and either a delay in treatment or the disease remaining untreated. Identifying retinopathy from retinal fundus image is a vital and diagnostic system performance depends on image quality and quantity. Furthermore, the diagnosis is prone to errors when a large imbalanced database is used. Hence, a fully automated retina disease prediction system is indispensable to minimize human intervention, increase the performance of the disease diagnostic system, and support ophthalmologists in conducting speedy and accurate investigations. Advancements in deep learning have remarkable results in identifying retinopathy from retinal fundus images. However, conventional deep-learning approaches struggle to learn enough in-depth features to identify aspects of mild retinal disease. To address this, integrates a deep autoencoder-based diagnostic system with a ResNet-based generative adversarial network (RGAN) to find retinal disease. This integrated model exploits a ResNet-50 structure to generate synthetic images to handle higher FAR and class imbalance-related problems and a deep autoencoder to categorize the retinal fundus pictures into benign and malicious. The proposed RGAN engenders synthetic images to train the diagnostic and real systems. The experimental outcomes have been implemented, and the recommended RGAN model increases the accuracy ratio of 95.6%, sensitivity ratio of 96.4%, specificity ratio of 97.3%, and F1-score ratio of 93.4% compared to other popular techniques.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107852"},"PeriodicalIF":4.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817354","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

Structural semantic enhancement network for low-dose CT denoising 低剂量CT去噪的结构语义增强网络

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-10 DOI: 10.1016/j.bspc.2025.107870

Jinwen He , Jiehang Deng , Zihang Hu , Guosheng Gu , Guoqing Qiao

{"title":"Structural semantic enhancement network for low-dose CT denoising","authors":"Jinwen He , Jiehang Deng , Zihang Hu , Guosheng Gu , Guoqing Qiao","doi":"10.1016/j.bspc.2025.107870","DOIUrl":"10.1016/j.bspc.2025.107870","url":null,"abstract":"<div><div>Most research on low-dose computed tomography primarily focuses on maximizing noise reduction, often at the expense of image structure and texture details. In this paper, we propose a Structural Semantic Enhancement Network (SSEN) that emphasizes the extraction and preservation of structural semantic features at different stages of the denoising process to enhance image sharpness. Specifically, unlike conventional methods that utilize a 3 × 3 Sobel operator for edge feature extraction, our approach employs a 5 × 5 Sobel operator with dense connections, preservingricher low-level semantics. Unlike conventional coordinate attention, which relies on 1 × 1 convolutional layers for feature activation, our approach employs 1 × 5 (or 5 × 1) asymmetric convolutional layers to expand the receptive field and capture richer global attention and contextual information. Furthermore, rather than commonly employed mean squared error loss functions, we propose a compound loss function that combines <em>L</em><sub>1</sub> loss, multi-scale structural similarity index measure loss, and multi-scale perceptual loss, effectively recovering structural and perceptual features. This study indicates that the proposed method can effectively extract and utilize the structural semantic features to retain more image structure and texture details. In the experiments on the AAPM-Mayo Clinic LDCT Grand Challenge dataset, SSEN achieved a SSIM of 0.9193 and a PSNR of 33.6191, outperforming the comparison methods in terms of image quality restoration and structural information recovery.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107870"},"PeriodicalIF":4.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808293","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

Prediction of weaning failure using time-frequency analysis of electrocardiographic and respiration flow signals 利用心电图和呼吸流信号的时频分析预测脱机失败

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-10 DOI: 10.1016/j.bspc.2025.107872

Hernando González Acevedo , José Luis Rodríguez-Sotelo , Carlos Arizmendi , Beatriz F. Giraldo

{"title":"Prediction of weaning failure using time-frequency analysis of electrocardiographic and respiration flow signals","authors":"Hernando González Acevedo , José Luis Rodríguez-Sotelo , Carlos Arizmendi , Beatriz F. Giraldo","doi":"10.1016/j.bspc.2025.107872","DOIUrl":"10.1016/j.bspc.2025.107872","url":null,"abstract":"<div><div>Acute respiratory distress syndrome often necessitates prolonged periods of mechanical ventilation for patient management. Therefore, it is crucial to make appropriate decisions regarding extubation to prevent potential harm to patients and avoid the associated risks of reintubation and extubation cycles. One atypical form of acute respiratory distress syndrome is associated with COVID-19, impacting patients admitted to the intensive care unit. This study presents the design of two classifiers: the first employs machine learning techniques, while the second utilizes a convolutional neural network. Their purpose is to assess whether a patient can safely be disconnected from a mechanical ventilator following a spontaneous breathing test. The machine learning algorithm uses descriptors derived from the variability of time-frequency representations computed with the non-uniform fast Fourier transform. These representations are applied to time series data, which consist of markers extracted from the electrocardiographic and respiratory flow signals sourced from the Weandb database. The input image for the convolutional neural network is formed by combining the spectrum of the RR signal and the spectrum of two parameters recorded from the respiratory flow signal, calculated using non-uniform fast Fourier transform. Three pre-trained network architectures are analyzed: Googlenet, Alexnet and Resnet-18. The best model is obtained with a CNN with the Resnet-18 architecture, presenting an accuracy of 90.1 ± 4.3%.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107872"},"PeriodicalIF":4.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808294","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

Automatic ECG signal quality assessment using convolutional neural networks and derivative ECG signal for false alarm reduction in wearable vital signs monitoring devices 基于卷积神经网络和导数心电信号的自动心电信号质量评估及其在可穿戴生命体征监测设备中的误报减少

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-08 DOI: 10.1016/j.bspc.2025.107876

Achinta Mondal , M. Sabarimalai Manikandan , Ram Bilas Pachori

{"title":"Automatic ECG signal quality assessment using convolutional neural networks and derivative ECG signal for false alarm reduction in wearable vital signs monitoring devices","authors":"Achinta Mondal , M. Sabarimalai Manikandan , Ram Bilas Pachori","doi":"10.1016/j.bspc.2025.107876","DOIUrl":"10.1016/j.bspc.2025.107876","url":null,"abstract":"<div><div>The electrocardiogram (ECG) signals are often analyzed to detect cardiovascular diseases and monitor vital signs. However, analysis of noisy ECG signals leads to misdiagnosis of diseases and generates false alarms. To prevent false alarms, we present a derivative ECG (dECG) signal-based lightweight convolutional neural network (CNN) for automatic ECG signal quality assessment (ECG-SQA). The proposed CNN detects clean (“acceptable”) and noisy (“unacceptable”) ECG signals which ensures only clean ECG signals are analyzed for disease detection and monitoring vital signs with reduced false alarms in health monitoring devices. Here, we evaluated the performance, total parameters, testing time for ECG-SQA, and model size of 60 dECG-based CNNs to determine the optimal ECG-SQA method. The performance of the dECG-based CNNs are analyzed with three activation functions, five kernel sizes, different numbers of convolutional layers, and dense layers. The CNNs are trained using ECG signals from one channel and fifteen channels of standard ECG databases. On a standard unseen ECG database, the proposed CNN model has achieved accuracy, sensitivity, and specificity of 97.59%, 98.78%, and 89.23%, respectively. The optimal CNN (model size: 2,989 kB) implemented on the Raspberry Pi computing platform has testing time of 130.44±46.24 ms for quality assessment of 5 s ECG signal which confirms the real-time feasibility of the proposed method. The dECG-based ECG-SQA method is essential during continuous monitoring of vital signs and diagnosis of cardiovascular disease to reduce false alarms and improve reliability of wearable devices having limited computing capacity and onboard memory.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107876"},"PeriodicalIF":4.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791947","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

A generative adversarial network for delineation of retinal interfaces in OCT B-scans with age-related macular degeneration 生成对抗网络描绘视网膜界面在OCT b扫描与年龄相关的黄斑变性

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-07 DOI: 10.1016/j.bspc.2025.107856

Himashree Kalita, Samarendra Dandapat, Prabin Kumar Bora

{"title":"A generative adversarial network for delineation of retinal interfaces in OCT B-scans with age-related macular degeneration","authors":"Himashree Kalita, Samarendra Dandapat, Prabin Kumar Bora","doi":"10.1016/j.bspc.2025.107856","DOIUrl":"10.1016/j.bspc.2025.107856","url":null,"abstract":"<div><div>Age-related macular degeneration (AMD) is a retinal disease that can impair the central vision permanently. Accurate delineation of the retinal pigment epithelium (RPE) and Bruch’s membrane (BM) in optical coherence tomography (OCT) B-scans is crucial for diagnosing and monitoring AMD. While automated segmentation methods exist for early AMD stages, late-stage AMD remains a challenging area due to the pronounced disruption of the RPE and BM. To ensure spatial contiguity in the boundary delineation of RPE and BM, both the global and local contextual information must be learned. In this context, we propose a generative adversarial network (GAN) to segment these significant retinal interfaces in OCT B-scans from AMD patients. A UNet++ model with its deep supervision is trained using a hybrid loss function combining adversarial loss and multi-class cross-entropy (CE) segmentation loss. The CE loss learns the local features by optimizing the per-pixel accuracy, while the adversarial loss captures a broader context by learning overall layer label statistics. This loss combination allows the model to capture fine details in the ordered retinal layer structure and guide layer boundaries along discontinuities in the RPE and BM in severe AMD cases. Additionally, a graph search algorithm refines boundary delineations from predicted segmentation maps. The model’s effectiveness is validated on the DUEIA and AROI datasets, which include OCT B-scans from both AMD-affected and healthy individuals. The proposed approach achieves Mean Absolute Errors (MAE) of 0.45 and 1.19 on the respective datasets, demonstrating its capability to handle boundary segmentation in severe AMD cases.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107856"},"PeriodicalIF":4.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785643","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

Narrowing the regional attention imbalance in medical image segmentation via feature decorrelation 基于特征去相关的医学图像分割中区域注意力不平衡的缩小

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2025-04-07 DOI: 10.1016/j.bspc.2025.107828

Mucong Zhuang , Yulin Li , Liying Hu , Zhiling Hong , Lifei Chen

引用次数: 0