Computer methods and programs in biomedicine最新文献

{"title":"A comprehensive benchmarking of a U-Net based model for midbrain auto-segmentation on transcranial sonography","authors":"Hong-yu Kang ,&nbsp;Wei Zhang ,&nbsp;Shuai Li ,&nbsp;Xinyi Wang ,&nbsp;Yu Sun ,&nbsp;Xin Sun ,&nbsp;Fang-Xian Li ,&nbsp;Chao Hou ,&nbsp;Sai-kit Lam ,&nbsp;Yong-ping Zheng","doi":"10.1016/j.cmpb.2024.108494","DOIUrl":"10.1016/j.cmpb.2024.108494","url":null,"abstract":"<div><h3>Background and objective</h3><div>Transcranial sonography-based grading of Parkinson's Disease has gained increasing attention in recent years, and it is currently used for assistive differential diagnosis in some specialized centers. To this end, accurate midbrain segmentation is considered an important initial step. However, current practice is manual, time-consuming, and bias-prone due to the subjective nature. Relevant studies in the literature are scarce and lacks comprehensive model evaluations from application perspectives. Herein, we aimed to benchmark the best-performing U-Net model for objective, stable and robust midbrain auto-segmentation using transcranial sonography images.</div></div><div><h3>Methods</h3><div>A total of 584 patients who were suspected of Parkinson's Disease were retrospectively enrolled from Beijing Tiantan Hospital. The dataset was divided into training (<em>n</em> = 416), validation (<em>n</em> = 104), and testing (<em>n</em> = 64) sets. Three state-of-the-art deep-learning networks (U-Net, U-Net+++, and nnU-Net) were utilized to develop segmentation models, under 5-fold cross-validation and three randomization seeds for safeguarding model validity and stability. Model evaluation was conducted in testing set in three key aspects: (i) segmentation agreement using DICE coefficients (DICE), Intersection over Union (IoU), and Hausdorff Distance (HD); (ii) model stability using standard deviations of segmentation agreement metrics; (iii) prediction time efficiency, and (iv) model robustness against various degrees of ultrasound imaging noise produced by the salt-and-pepper noise and Gaussian noise.</div></div><div><h3>Results</h3><div>The nnU-Net achieved the best segmentation agreement (averaged DICE: 0.910, IoU: 0.836, HD: 2.793-mm) and time efficiency (1.456-s). Under mild noise corruption, the nnU-Net outperformed others with averaged scores of DICE (0.904), IoU (0.827), HD (2.941 mm) in the salt-and-pepper noise (signal-to-noise ratio, SNR = 0.95), and DICE (0.906), IoU (0.830), HD (2.967 mm) in the Gaussian noise (sigma value, σ = 0.1); by contrast, intriguingly, performance of the U-Net and U-Net+++ models were remarkably degraded. Under increasing levels of simulated noise corruption (SNR decreased from 0.95 to 0.75; σ increased from 0.1 to 0.5), the nnU-Net network exhibited marginal decline in segmentation agreement meanwhile yielding decent performance as if there were absence of noise corruption.</div></div><div><h3>Conclusions</h3><div>The nnU-Net model was the best-performing midbrain segmentation model in terms of segmentation agreement, stability, time efficiency and robustness, providing the community with an objective, effective and automated alternative. Moving forward, a multi-center multi-vendor study is warranted when it comes to clinical implementation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108494"},"PeriodicalIF":4.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616196","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}

{"title":"DeepForest-HTP: A novel deep forest approach for predicting antihypertensive peptides","authors":"Qiyuan Bai ,&nbsp;Hao Chen ,&nbsp;Wenshuo Li ,&nbsp;Lei Li ,&nbsp;Junhao Li ,&nbsp;Zhen Gao ,&nbsp;Yuan Li ,&nbsp;Xuhua Li ,&nbsp;Bing Song","doi":"10.1016/j.cmpb.2024.108514","DOIUrl":"10.1016/j.cmpb.2024.108514","url":null,"abstract":"<div><div>Hypertension is a major preventable risk factor for cardiovascular disease, affecting over 1.5 billion adults worldwide. Antihypertensive peptides (AHTPs) have gained attention as a natural therapeutic option with minimal side effects. This study proposes a Deep Forest-based machine learning framework for AHTP prediction, leveraging a multi-granularity cascade structure to enhance classification accuracy. We integrated data from BIOPEP-UWM and three previously used datasets, totaling 2000 peptide sequences, and introduced novel feature extraction methods to build a comprehensive dataset for model training.</div><div>This study represents the first application of Deep Forest for AHTP identification, demonstrating substantial classification performance advantages over traditional methods (e.g., SVM, CNN, and XGBoost) as well as recent mainstream prediction models (Ensemble-AHTPpred, CNN-SVM Ensemble, and mAHTPred). Requiring no complex manual feature engineering, the model adapts flexibly to various data needs, offering a novel perspective for efficient AHTP prediction and promising utility in hypertension management.</div><div>On the benchmark dataset, the model achieved high accuracy, sensitivity, and AUC, providing a robust tool for identifying safe and effective AHTPs. However, future efforts should incorporate larger and more diverse independent validation datasets to further improve the model and enhance its generalizability. Additionally, the model's predictive accuracy relies on known AHTP targets and sequence features, potentially limiting its ability to detect AHTPs with uncharacterized or atypical properties.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108514"},"PeriodicalIF":4.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positional encoding-guided transformer-based multiple instance learning for histopathology whole slide images classification","authors":"Jun Shi ,&nbsp;Dongdong Sun ,&nbsp;Kun Wu ,&nbsp;Zhiguo Jiang ,&nbsp;Xue Kong ,&nbsp;Wei Wang ,&nbsp;Haibo Wu ,&nbsp;Yushan Zheng","doi":"10.1016/j.cmpb.2024.108491","DOIUrl":"10.1016/j.cmpb.2024.108491","url":null,"abstract":"<div><h3>Background and objectives:</h3><div>Whole slide image (WSI) classification is of great clinical significance in computer-aided pathological diagnosis. Due to the high cost of manual annotation, weakly supervised WSI classification methods have gained more attention. As the most representative, multiple instance learning (MIL) generally aggregates the predictions or features of the patches within a WSI to achieve the slide-level classification under the weak supervision of WSI labels. However, most existing MIL methods ignore spatial position relationships of the patches, which is likely to strengthen the discriminative ability of WSI-level features.</div></div><div><h3>Methods:</h3><div>In this paper, we propose a novel positional encoding-guided transformer-based multiple instance learning (PEGTB-MIL) method for histopathology WSI classification. It aims to encode the spatial positional property of the patch into its corresponding semantic features and explore the potential correlation among the patches for improving the WSI classification performance. Concretely, the deep features of the patches in WSI are first extracted and simultaneously a position encoder is used to encode the spatial 2D positional information of the patches into the spatial-aware features. After incorporating the semantic features and spatial embeddings, multi-head self-attention (MHSA) is applied to explore the contextual and spatial dependencies of the fused features. Particularly, we introduce an auxiliary reconstruction task to enhance the spatial–semantic consistency and generalization ability of features.</div></div><div><h3>Results:</h3><div>The proposed method is evaluated on two public benchmark TCGA datasets (TCGA-LUNG and TCGA-BRCA) and two in-house clinical datasets (USTC-EGFR and USTC-GIST). Experimental results validate it is effective in the tasks of cancer subtyping and gene mutation status prediction. In the test stage, the proposed PEGTB-MIL outperforms the other state-of-the-art methods and respectively achieves 97.13±0.34%, 86.74±2.64%, 83.25±1.65%, and 72.52±1.63% of the area under the receiver operating characteristic (ROC) curve (AUC).</div></div><div><h3>Conclusion:</h3><div>PEGTB-MIL utilizes positional encoding to effectively guide and reinforce MIL, leading to enhanced performance on downstream WSI classification tasks. Specifically, the introduced auxiliary reconstruction module adeptly preserves the spatial–semantic consistency of patch features. More significantly, this study investigates the relationship between position information and disease diagnosis and presents a promising avenue for further research.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108491"},"PeriodicalIF":4.9,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643509","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}

{"title":"Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis","authors":"Meri Ferretti ,&nbsp;Valentina D.A. Corino","doi":"10.1016/j.cmpb.2024.108496","DOIUrl":"10.1016/j.cmpb.2024.108496","url":null,"abstract":"<div><h3>Background and objectives</h3><div>The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer.</div></div><div><h3>Methods</h3><div>Four-hundred twenty-two patients from “Lung1” dataset were included in the study. A 3D convolutional autoencoder (AE) was built and features from the latent space extracted for further analysis. Radiomic features were derived from the 3D volume of the tumor region using PyRadiomics. Both radiomic and AE-based features underwent feature selection, by removing: i) highly correlated and ii) constant features. The selected variables were then used to derive both mono-domain (radiomics, AE and clinic) and multi-domain signatures fitting a Cox Proportional Hazard model with LASSO penalization and evaluated considering the concordance (C)-index as performance metric.</div></div><div><h3>Results</h3><div>Both mono-domain and multi-domain signatures could significantly differentiate high risk from low risk patients. Among the mono-domain signatures, the highest hazard ratio (HR) in the test set was obtained using radiomics (HR = 1.5428) followed by the AE-based signature (HR = 1.5012) and the clinical signature (HR = 1.4770). The best overall performance was achieved by combining all three signatures, resulting in the highest HR (HR = 1.7383), while the combination of AE-based and clinical signatures yielded the highest C-index (C-index = 0.6309).</div></div><div><h3>Conclusions</h3><div>These preliminary results show that combining information carried by AE, radiomic and clinical domain shows potential for improving the prediction of overall survival in NSCLC patients.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108496"},"PeriodicalIF":4.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer model coupling hemodynamics and oxygen transport in the coronary capillary network: Pulsatile vs. non-pulsatile analysis","authors":"Haifeng Wang ,&nbsp;Jenny S. Choy ,&nbsp;Ghassan S. Kassab ,&nbsp;Lik-Chuan Lee","doi":"10.1016/j.cmpb.2024.108486","DOIUrl":"10.1016/j.cmpb.2024.108486","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Oxygen transport in the heart is crucial, and its impairment can lead to pathological conditions such as hypoxia, ischemia, and heart failure. However, investigating oxygen transport in the heart using <em>in vivo</em> measurements is difficult due to the small size of the coronary capillaries and their deep embedding within the heart wall.</div></div><div><h3>Methods:</h3><div>In this study, we developed a novel computational modeling framework that integrates a 0-D hemodynamic model with a 1-D mass transport model to simulate oxygen transport in/across the coronary capillary network.</div></div><div><h3>Results:</h3><div>The model predictions agree with analytical solutions and experimental measurements. The framework is used to simulate the effects of pulsatile vs. non-pulsatile behavior of the capillary hemodynamics on oxygen-related metrics such as the myocardial oxygen consumption (<span><math><msub><mrow><mtext>MVO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span>) and oxygen extraction ratio (OER). Compared to simulations that consider (physiological) pulsatile behaviors of the capillary hemodynamics, the OER is underestimated by less than 9% and the <span><math><msub><mrow><mtext>MVO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> is overestimated by less than 5% when the pulsatile behaviors are ignored in the simulations. Statistical analyses show that model predictions of oxygen-related quantities and spatial distribution of oxygen without consideration of the pulsatile behaviors do not significantly differ from those that considered such behaviors (p-values <span><math><mrow><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>).</div></div><div><h3>Conclusions:</h3><div>This finding provides the basis for reducing the model complexity by ignoring the pulsatility of coronary capillary hemodynamics in the computational framework without a substantial loss of accuracy when predicting oxygen-related metrics.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108486"},"PeriodicalIF":4.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643484","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}

{"title":"A computational method to predict cerebral perfusion flow after endovascular treatment based on invasive pressure and resistance","authors":"Xi Zhao ,&nbsp;Li Bai ,&nbsp;Raynald ,&nbsp;Jie He ,&nbsp;Bin Han ,&nbsp;Xiaotong Xu ,&nbsp;Zhongrong Miao ,&nbsp;Dapeng Mo","doi":"10.1016/j.cmpb.2024.108510","DOIUrl":"10.1016/j.cmpb.2024.108510","url":null,"abstract":"<div><h3>Background and objective</h3><div>Predicting post-operative flow is essential for assessing the risk of adverse events in cerebrovascular stenosis patients following endovascular treatment (EVT). This study aimed to evaluate the accuracy of the CFD simulation model in predicting post-operative velocity, flow and pressure distal to a stenosis, based on cerebrovascular microcirculatory resistance.</div></div><div><h3>Methods</h3><div>The patient-specific models of the extracranial and intracranial arteries were reconstructed. The cerebrovascular microcirculatory resistance was applied to estimate post-operative blood velocity and flow rates. Pearson correlation and Bland-Altman analyses were used to evaluate the correlation and agreement between CFD calculations and transcranial Doppler (TCD) measurements.</div></div><div><h3>Results</h3><div>There was a strong correlation between CFD- and TCD-based mean velocities (<em>r</em> = 0.7733; <em>P</em> = 0.0002), with volume flow measured by both methods also showing robust correlation (<em>r</em> = 0.8621; <em>P</em> &lt; 0.0001). Additionally, agreement was found between mean velocities determined by CFD simulation and those estimated by TCD (<em>P</em> = 0.2446, mean difference −4.2089; limits of agreement -11.5764 to 3.1586). However, agreement between volume flow from CFD simulations and TCD was less consistent (<em>P</em> = 0.0387, mean difference -0.3272, limits of agreement -0.9276 to 0.2731).</div></div><div><h3>Conclusions</h3><div>The computational method used in this study enables the prediction of hemodynamic changes and offers valuable support in tailoring treatment strategies for cerebrovascular stenosis lesions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108510"},"PeriodicalIF":4.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643466","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}

{"title":"Localizing the seizure onset zone and predicting the surgery outcomes in patients with drug-resistant epilepsy: A new approach based on the causal network","authors":"Mingming Chen,&nbsp;Kunlin Guo,&nbsp;Kai Lu,&nbsp;Kunying Meng,&nbsp;Junfeng Lu,&nbsp;Yajing Pang,&nbsp;Lipeng Zhang,&nbsp;Yuxia Hu,&nbsp;Renping Yu,&nbsp;Rui Zhang","doi":"10.1016/j.cmpb.2024.108483","DOIUrl":"10.1016/j.cmpb.2024.108483","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Accurate localization of the seizure onset zone (SOZ) is crucial for surgical treatment in patients with drug-resistant epilepsy (DRE). However, clinical identification of SOZ often relies on physician experience and has a certain subjectivity. Therefore, it is emergent to develop quantitative computational tools to assist clinicians in identifying SOZ.</div></div><div><h3>Methods:</h3><div>We conduct a retrospective study on intracranial electroencephalography (iEEG) data from 46 patients with DRE. The interactions between different brain regions are quantified by using the phase transfer entropy (PTE), based on which the causal influence index (CII) is proposed to quantify the degree of influence of nodes on the network. Subsequently, the features extracted by the CII are used to construct a random forest classification model, which the performance in identifying SOZ and the generalizability are validated in patients with successful surgeries. Then, based on the CII features of the clinically labeled SOZ, a logistic regression prediction model is constructed to predict the probability of surgical success. The statistical analysis between patients with successful and failed surgery is conducted with the Mann–Whitney U test. Finally, the consistency between the predicted SOZ and the clinically labeled SOZ is verified across different Engel classes.</div></div><div><h3>Results:</h3><div>The classification model combining the low-frequency and high-frequency features can achieve an accuracy of 82.18% (sensitivity: 85.01%, specificity: 79.69%) and an area under curve (AUC) of 0.90 in identifying SOZ. Furthermore, the model exhibits strong generalizability in identifying SOZ in patients with MRI lesional and non-lesional, as well as those implanted with electrocorticography (ECOG) and stereotactic EEG (SEEG) electrodes. Moreover, the prediction model could achieve an average accuracy of 79.8% and an AUC of 0.84. Of note, the prediction of surgical success probability is significant between patients with successful and failed surgeries (P<span><math><mo>&lt;</mo></math></span>0.001). Correspondingly, the highest consistency between model-predicted SOZ and clinically labeled SOZ can be observed in patients with successful surgeries, but this consistency gradually decreases with increasing Engel classes.</div></div><div><h3>Conclusions:</h3><div>These results demonstrate that the CII may be a potential biomarker for identifying the SOZ in patients with DRE, which may provide a new perspective for the treatment of epilepsy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108483"},"PeriodicalIF":4.9,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616231","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}

{"title":"A multi-scale, multi-task fusion UNet model for accurate breast tumor segmentation","authors":"Shuo Dai ,&nbsp;Xueyan Liu ,&nbsp;Wei Wei ,&nbsp;Xiaoping Yin ,&nbsp;Lishan Qiao ,&nbsp;Jianing Wang ,&nbsp;Yu Zhang ,&nbsp;Yan Hou","doi":"10.1016/j.cmpb.2024.108484","DOIUrl":"10.1016/j.cmpb.2024.108484","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Breast cancer is the most common cancer type among women worldwide and a leading cause of female death. Accurately interpreting these complex tumors, involving small size and morphology, requires a significant amount of expertise and time. Developing a breast tumor segmentation model to assist clinicians in treatment, therefore, holds great practical significance.</div></div><div><h3>Methods:</h3><div>We propose a multi-scale, multi-task model framework named MTF-UNet. Firstly, we differ from the common approach of using different convolution kernel sizes to extract multi-scale features, and instead use the same convolution kernel size with different numbers of convolutions to obtain multi-scale, multi-level features. Additionally, to better integrate features from different levels and sizes, we extract a new multi-branch feature fusion block (ADF). This block differs from using channel and spatial attention to fuse features, but considers fusion weights between various branches. Secondly, we propose to use the number of pixels predicted to be related to tumors and background to assist segmentation, which is different from the conventional approach of using classification tasks to assist segmentation.</div></div><div><h3>Results:</h3><div>We conducted extensive experiments on our proprietary DCE-MRI dataset, as well as two public datasets (BUSI and Kvasir-SEG). In the aforementioned datasets, our model achieved excellent MIoU scores of 90.4516%, 89.8408%, and 92.8431% on the respective test sets. Furthermore, our ablation study has demonstrated the efficacy of each component and the effective integration of our auxiliary prediction branch into other models.</div></div><div><h3>Conclusion:</h3><div>Through comprehensive experiments and comparisons with other algorithms, the effectiveness, adaptability, and robustness of our proposed method have been demonstrated. We believe that MTF-UNet has great potential for further development in the field of medical image segmentation. The relevant code and data can be found at <span><span>https://github.com/LCUDai/MTF-UNet.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108484"},"PeriodicalIF":4.9,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616210","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}

{"title":"Effect of myogenic tone on agonist-mediated vasoconstriction in isolated arteries: A computational study","authors":"Ranjan K. Pradhan","doi":"10.1016/j.cmpb.2024.108495","DOIUrl":"10.1016/j.cmpb.2024.108495","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background and objective&lt;/h3&gt;&lt;div&gt;Vasoconstriction of the resistance artery is mainly determined by an integrated action of multiple local stimuli acting on the vascular smooth muscle cells, which include neuronal delivery of &lt;em&gt;α&lt;/em&gt;-adrenoceptor agonists and intraluminal pressure. The contractile activity of the arterial wall has been extensively studied &lt;em&gt;ex vivo&lt;/em&gt; using isolated arterial preparations and myography techniques. However, agonist-mediated vasoconstriction response is often confounded by local effects of other stimuli (e.g., pressure) and, it remained unclear whether the pressure-induced myogenic response has any implication on the efficacy of agonist-mediated vasoconstriction during blood flow regulation in tissues. A quantitative understanding of the influence of each stimulus is necessary to understand the interaction between multiple regulatory mechanisms, which is required to ensure timely oxygen delivery to meet tissue needs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We developed a simple empirical model of isolated vessel vasoreactivity that includes passive vessel wall mechanics and a lumped representation of active smooth muscle activation as a function of agonist concentration and pressure. Pressure myograph data in dog renal arterioles and rat femoral arterioles, isovolumic myograph data in rat femoral arteries, and vasoactive data in rat skeletal muscle arterioles were analyzed using the model. The effect of physiological pressure changes on the sensitivities of vascular segments to adrenergic agonists phenylephrine and norepinephrine was evaluated.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Model-based analysis of isolated vasoreactivity data, obtained due to changes in pressure and vasoconstricting agonists revealed that the strength of myogenic response of a resistance vessel has a strong influence on the sensitivity and dynamics of agonist response. An increase in intraluminal pressure was found to reduce the magnitude of agonist-mediated tone by lowering the sensitivity of the vessel segment to agonist. The passive mechanical properties of arterial wall considearably influence the agonist-mediated contraction in isolated arteries. These results demonstrate how passive vessel wall mechanics may dominate the vasoactive responses of the common myogenic and adrenergic pathways of smooth muscle contraction in blood flow regulation, supporting a long standing notion that there exists segment-specific vasoregulation in microvascular networks of various tissues.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;The present model provides a simple and powerful tool for quantifying &lt;em&gt;ex vivo&lt;/em&gt; vasoreactivity of asolated arteries to qualitatively study the interaction between myogenic and &lt;em&gt;α&lt;/em&gt;-adrenergic control of vascular tone in isolated vessels. Analysis of pressure myography data and isovolumic myography data in different sizes of vessels and tissues, in response to norepinephrine and phenylephrine revealed the im","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108495"},"PeriodicalIF":4.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616211","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}

{"title":"Segmentation of infarct lesions and prognosis prediction for acute ischemic stroke using non-contrast CT scans","authors":"Xuechun Wang ,&nbsp;Yuting Meng ,&nbsp;Zhijian Dong ,&nbsp;Zehong Cao ,&nbsp;Yichu He ,&nbsp;Tianyang Sun ,&nbsp;Qing Zhou ,&nbsp;Guozhong Niu ,&nbsp;Zhongxiang Ding ,&nbsp;Feng Shi ,&nbsp;Dinggang Shen","doi":"10.1016/j.cmpb.2024.108488","DOIUrl":"10.1016/j.cmpb.2024.108488","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Ischemic stroke is the most common type of stroke and the second leading cause of global mortality. Prompt and accurate diagnosis is crucial for effective treatment. Non-contrast CT (NCCT) scans are commonly employed as the first-line imaging modality to identify the infarct lesion and affected brain areas, as well as to make prognostic predictions to guide the subsequent treatment planning. However, visual evaluation of infarct lesions in NCCT scans can be subjective and inconsistent due to reliance on expert experience.</div></div><div><h3>Methods</h3><div>In this study, we propose an automatic method using VB-Net with dual-channel inputs to segment acute infarct lesions (AIL) on NCCT scans and extract affected ASPECTS (Alberta Stroke Program Early CT Score) regions. Secondly, we establish a prediction model to distinguish reperfused patients from non-reperfused patients after treatment, based on multi-dimensional radiological features of baseline NCCT and stroke onset time. Thirdly, we create a prediction model estimating the infarct volume after a period of time, by combining NCCT infarct volume, radiological features, and surgical decision.</div></div><div><h3>Results</h3><div>The median Dice coefficient of the AIL segmentation network is 0.76. Based on this, the patient triage model has an AUC of 0.837 (95 % confidence interval [CI]: 0.734–0.941), sensitivity of 0.833 (95 % CI: 0.626–0.953). The predicted follow-up infarct volume correlates strongly with the DWI ground truth, with a Pearson correlation coefficient of 0.931.</div></div><div><h3>Conclusions</h3><div>Our proposed pipeline offers qualitative and quantitative assessment of infarct lesions based on NCCT scans, facilitating physicians in patient triage and prognosis prediction.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"258 ","pages":"Article 108488"},"PeriodicalIF":4.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616198","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}