Computer methods and programs in biomedicine最新文献

{"title":"MultiExCam: A multi approach and explainable artificial intelligence architecture for skin lesion classification","authors":"Tommaso Ruga ,&nbsp;Luciano Caroprese ,&nbsp;Eugenio Vocaturo ,&nbsp;Ester Zumpano","doi":"10.1016/j.cmpb.2025.109081","DOIUrl":"10.1016/j.cmpb.2025.109081","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Cutaneous melanoma remains the most lethal form of skin cancer. Although incurable at advanced stages, if diagnosed at an early, localized stage, the five-year survival rate is remarkably high. Recent advancements in artificial intelligence have paved the way for early skin lesion diagnosis, leveraging digital imaging processes into effective solutions. Most of these, however, use Machine Learning and Deep Learning techniques compartmentalized, without combining the produced predictions.</div></div><div><h3>Methods:</h3><div>This paper introduces MultiExCam, a novel multi approach and explainable architecture for skin cancer detection that integrates both machine and deep learning. Three heterogeneous data from three different techniques are used: dermatoscopic images, features extracted from deep learning techniques, and hand-crafted statistical features. A convolutional neural network is used for both deep feature extraction and initial classification, with the extracted features being combined with handcrafted ones to train four additional machine learning models. An advanced ensemble model, implemented as a Feed Forward Neural Network with gating and attention mechanism, produces the final classification. To enhance interpretability, the architecture employs GradCAM for visualizing critical regions in input images and SHAP for evaluating the contribution of individual features to predictions.</div></div><div><h3>Results:</h3><div>MultiExCam demonstrates robust performance across three diverse datasets (HAM10000, ISIC, MED-NODE), achieving AUC scores of 97%, 91%, and 98% respectively, with corresponding F1-scores of 92%, 87%, and 94%. Comprehensive ablation studies validate the importance of the preprocessing pipeline and ensemble integration, with the hybrid approach consistently outperforming baseline deep learning models by 1–3 percentage points. Unlike existing compartmentalized hybrid solutions, MultiExCam’s adaptive ensemble architecture learns personalized decision strategies for individual lesions, mimicking expert dermatological workflows that integrate multiple evidence sources. The explainability analysis reveals clinically meaningful activation patterns corresponding to established diagnostic criteria including asymmetry, border irregularity, and color variation.</div></div><div><h3>Conclusion:</h3><div>MultiExCam establishes a new paradigm for AI-assisted dermatological diagnosis by demonstrating that true hybrid integration of deep learning and machine learning, combined with comprehensive explainability techniques, can achieve both superior diagnostic performance and clinical interpretability. The architecture’s ability to provide accurate classifications while explaining prediction rationale addresses critical requirements for medical AI adoption, offering a promising foundation for clinical decision support systems in melanoma detection.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"273 ","pages":"Article 109081"},"PeriodicalIF":4.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181374","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":"Beyond predictive accuracy: Statistical validation of feature importance in biomedical machine learning","authors":"Souichi Oka ,&nbsp;Nobuko Inoue ,&nbsp;Yoshiyasu Takefuji","doi":"10.1016/j.cmpb.2025.109085","DOIUrl":"10.1016/j.cmpb.2025.109085","url":null,"abstract":"<div><div>In medical machine learning (ML), a fundamental methodological distinction exists between optimizing model performance for predictive tasks and pursuing causal inference for mechanistic interpretation. Achieving high predictive accuracy does not necessarily imply that a model can uncover the true physiological mechanisms underlying the data. This letter addresses a critical interpretational challenge in medical machine learning, building upon Yuyang Yan et al.’s valuable work on exacerbation classification in asthma and COPD. While their multi-feature fusion model, particularly comprising models such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Bidirectional Long Short-Term Memory (BiLSTM) demonstrates high predictive accuracy for respiratory exacerbations, we highlight that such performance alone does not guarantee reliable insights into feature importance. Complex tree-based models like RF, when interpreted via methods like SHapley Additive exPlanations (SHAP), can exhibit inherent biases, overemphasizing features used in early splits and reflecting what is important for their specific prediction rather than the true underlying physiological drivers. Validating feature importance remains challenging without ground truth, as different models often yield varying rankings. We argue that solely relying on model-dependent interpretations risks misrepresenting the actual mechanisms of complex medical phenomena. Therefore, we advocate for a robust analytical strategy that transcends mere predictive metrics. This involves a synergistic approach combining the predictive power of ML with impartial, complementary statistical methodologies—such as non-parametric correlation and mutual information—to ensure genuinely trustworthy scientific insights into the true drivers of respiratory exacerbations.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109085"},"PeriodicalIF":4.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155302","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":"High-order mesoscale modeling with geometrically conforming gray/white matter interface for traumatic brain injury","authors":"Denis Molchanov ,&nbsp;Chaokai Zhang ,&nbsp;Nan Lin ,&nbsp;Songbai Ji ,&nbsp;Zhangxian Yuan","doi":"10.1016/j.cmpb.2025.109074","DOIUrl":"10.1016/j.cmpb.2025.109074","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Brain injury models with a supramillimeter resolution are not feasible to provide spatially detailed strains at or below millimeter scales, especially in regions of convoluted geometry such as at the gray/white matter interface. Furthermore, non-conforming mesh boundaries resulting from discretization errors can lead to inaccurate strain and stress distributions near interfaces, areas typically associated with elevated vulnerabilities in traumatic brain injury (TBI). Conventional approaches using extremely small linear elements are not effective to address the issue because of challenges in generating boundary-conforming meshes and slow convergence.</div></div><div><h3>Methods:</h3><div>In this study, we adapt the Non-Uniform Rational B-Splines (NURBS) and isogeometric analysis (IGA) to develop high-order mesoscale models that smoothly represent complex tissue boundaries with highly resolved strain distributions. We address key challenges for applications to the brain, including the construction of smooth tissue boundaries from voxelized image segmentation and overcoming numerical difficulties arising from near-incompressibility.</div></div><div><h3>Results:</h3><div>Compared to the conventional model using linear elements, the high-order mesoscale model demonstrates superior efficiency by achieving the same accuracy but with two orders of magnitude fewer degrees of freedom and at least one order of magnitude reduction in computational cost. Two-dimensional mesoscale models are constructed at gray/white matter interface to simulate realistic impact loading. The high-order mesoscale models discover strain concentration at the convoluted tissue boundary missing from the global model (e.g., up to 20% difference in magnitude). Notable differences in strain distribution also exist, with a normalized root mean squared error of up to 7.7% for strains sampled near the interface. These strain differences have major implications on downstream axonal injury model simulations.</div></div><div><h3>Conclusion:</h3><div>This study demonstrates the unique potential of leveraging IGA to develop mesoscale brain models with conforming tissue boundaries, and is important for filling a critical gap between global and cellular brain injury models in a multiscale modeling framework. The technique is general and scalable as it is applicable to diverse two- and three-dimensional biomechanical problems, including and beyond brain biomechanics.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109074"},"PeriodicalIF":4.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155376","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":"Whole-brain computational modeling reveals disruption of microscale brain dynamics in Parkinson’s disease","authors":"Wenqian Zhao ,&nbsp;Xueao Li ,&nbsp;Jing Wang ,&nbsp;Chunyan Zhang ,&nbsp;Yuchuan Zhuang ,&nbsp;Yanbo Dong ,&nbsp;Andrey Tulupov ,&nbsp;Jing Li ,&nbsp;Fengshou Zhang ,&nbsp;Jianfeng Bao","doi":"10.1016/j.cmpb.2025.109076","DOIUrl":"10.1016/j.cmpb.2025.109076","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Parkinson’s disease (PD) alters the brain’s neurodynamic properties, contributing to both motor and non-motor symptoms. Although advances in neuroimaging techniques—such as resting-state functional MRI (rsfMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI)—have enhanced our understanding of brain structure and function, they remain limited in detecting subtle, region-specific dynamic alterations associated with functional deficits. This study aims to apply the relaxed mean field dynamic modeling (rMFM) to identify microscale dynamic abnormalities in PD and to link these changes with network topology and clinical characteristics.</div></div><div><h3>Methods</h3><div>We employed the rMFM, a biophysically informed computational framework that integrates structural and functional imaging data with microstructural features to simulate local dynamics of brain regions. Unlike traditional models, rMFM allows the optimization of regional recurrent connection strength w and subcortical input I, thereby capturing inter-regional heterogeneity more effectively. Separate rMFM models were constructed for the PD and healthy control (HC) groups. Group differences in model parameters were assessed, followed by graph-theoretical analysis to examine alterations in brain network topology. Correlation analyses were also performed to investigate the relationships between model parameters, network metrics, and clinical variables.</div></div><div><h3>Results</h3><div>Significant alterations in w and I were observed in regions such as the middle temporal gyrus and banks of the superior temporal sulcus (bankssts) in the PD group, suggesting localized dynamic disruptions related to language, memory, and cognitive impairments. Corresponding alterations in brain network topology accompanied these parameter changes. At the same time, the results of graph theory analysis suggest that in early PD, functional disorders may appear before obvious structural changes.</div></div><div><h3>Conclusions</h3><div>This study introduces rMFM as an innovative approach for modeling local brain dynamics by integrating multimodal MRI data with microscale neural features. The findings highlight distinctive microscale dynamic abnormalities in PD and their linkage to large-scale network changes. This approach enhances our understanding of PD pathophysiology and provids a basis for identifying potential disease-specific biomarkers.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109076"},"PeriodicalIF":4.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155377","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":"Measuring phase-amplitude coupling using dispersion fuzzy mutual information","authors":"Hao Zhang ,&nbsp;Zhijie Bian ,&nbsp;Xiaonan Guo ,&nbsp;Xiaoli Li ,&nbsp;Shimin Yin ,&nbsp;Dong Cui","doi":"10.1016/j.cmpb.2025.109075","DOIUrl":"10.1016/j.cmpb.2025.109075","url":null,"abstract":"<div><h3>Background</h3><div>Mild Cognitive Impairment (MCI) is a preliminary stage of Alzheimer’s disease (AD), and early diagnosis of MCI electroencephalography (EEG) signals using the Phase-Amplitude Coupling (PAC) phenomenon in neural oscillations as an EEG marker has become a promising technique. Nonetheless, the PAC estimators, which are frequently employed in clinical practice, exhibit considerable limitations with regard to their application conditions. In order to explore a PAC estimator with strong applicability, the Dispersion Fuzzy Mutual Information (DFMI) method is proposed.</div></div><div><h3>Methods</h3><div>The DFMI method employs the symbolization principle of dispersion entropy and mutual information theory to process time series. This approach addresses the challenges posed by ambiguous quantitative fluctuations in the number of patterns in fuzzy entropy and upgrades the single-channel fuzzy entropy algorithm to a dual-channel DFMI algorithm. Subsequently, through simulation analysis, it was compared with the commonly used PAC estimator in clinical practice in terms of coupling strength sensitivity, data length dependency, noise resistance, coupling frequency band sensitivity, and artifact resistance.</div></div><div><h3>Results</h3><div>The simulation results indicate that the DFMI method can effectively obtain PAC strength, is less dependent on data length, produces stable calculation results, and is less affected by pseudo-trace signals. The MCI-EEG data results demonstrated that MCI patients significantly enhanced whole-brain theta-gamma coupling activity, while alpha-gamma coupling activity shifted to the low-frequency band.</div></div><div><h3>Conclusion</h3><div>The DFMI can be utilized as a PAC estimator to assess PAC phenomena in neural signals, and the coupling of neural oscillations in the MCI brain may manifest as coupling band attenuation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109075"},"PeriodicalIF":4.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148137","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":"Real-time tracking and inpainting network with joint learning iterative modules for AR-based DALK surgical navigation","authors":"Weimin Liu ,&nbsp;Junjun Pan ,&nbsp;Liyun Jia ,&nbsp;Sijing Rao ,&nbsp;Jie Zang","doi":"10.1016/j.cmpb.2025.109068","DOIUrl":"10.1016/j.cmpb.2025.109068","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Deep anterior lamellar keratoplasty (DALK) is a widely used treatment for eye diseases and requires accurate and even stitch positions during the suturing process. In this regard, the utilization of Augmented Reality (AR) navigation systems shows promising potential in enhancing the stitching process, and a clear and unoccluded view of the corneal regions would help surgeons better plan the stitching positions.</div></div><div><h3>Methods:</h3><div>In this work, we present a joint-learning and iterative network for AR-based suturing navigation. This network aims to improve the performance of the inpainting under serious occlusion in the suturing process. And it can provide both original instruments and inpainted corneal masks along with inpainted frames. The network is based on feature reuse, iterative modules, and mask propagation structures to greatly reduce the computational cost. For the requirement of end-to-end training, we also propose a novel dataset synthesis method to construct a dataset with both occluded and unoccluded image pairs, along with mask and optical flow annotations. We also develop a novel pipeline based on the grid propagation method and inpainted optical flow outputs to provide clear and stable inpainted frames.</div></div><div><h3>Results:</h3><div>Based on the synthetic datasets, compared to the recent outstanding inpainting networks, our framework reaches a better trade-off between performance and computation efficiency. Our Iter-S model finally gets a mean endpoint error (mEPE) of 1.69, a peak signal-to-noise ratio (PSNR) of 36.86, and a structure similarity index measure (SSIM) of 0.976, along with a low inpainting inference time of 16.26ms. Based on the Iter-S, we construct a novel AR navigation system with a frame rate of around 35.14ms/28FPS on average.</div></div><div><h3>Conclusions:</h3><div>The iterative modules can progressively refine the outputs while providing a favorable trade-off between visual performance and real-time computation efficiency based on the selection of iteration times. Our AR navigation framework can provide stable and accurate tracking outputs with well-inpainted results in real time under severe occlusion conditions, which demonstrates the benefits of guiding the stitching operations of surgeons in corneal surgeries.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109068"},"PeriodicalIF":4.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119198","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":"Adaptive gas supply system for membrane oxygenator using online model identification and control in normothermic machine perfusion","authors":"Shiwei Wang ,&nbsp;Junwei Jiang ,&nbsp;Jie Hou ,&nbsp;Xirong Liao ,&nbsp;Zhiyong Huang ,&nbsp;Xiaoyu Li ,&nbsp;Jiang Zhang ,&nbsp;Daidi Zhong ,&nbsp;Pan Yang","doi":"10.1016/j.cmpb.2025.109026","DOIUrl":"10.1016/j.cmpb.2025.109026","url":null,"abstract":"<div><h3>Objective:</h3><div>This work addresses a critical challenge in normothermic machine perfusion (NMP) : the precise and safe control of the oxygenator’s gas supply. The objective is to develop a novel control framework that integrates real-time model identification with adaptive pressure control, aiming to dynamically regulate the partial pressure of oxygen in the blood while preventing critical failure modes like plasma leakage.</div></div><div><h3>Methods:</h3><div>By analyzing the oxygenation process within the artificial lung membrane, we demonstrate that the gas supply system’s input–output behavior can be modeled using a discrete-time autoregressive model with exogenous input (ARX). In this model, the concentrations of both gas and liquid phases are related to the gradients of transmembrane pressure. An online parameter identification employs a forgetting factor recursive least squares (FFRLS) algorithm to control the transmembrane pressure difference. The algorithm enables adaptive tuning of a proportional–integral–derivative (PID) controller, and controller parameters are dynamically updated using real-time model estimates. This adaptive mechanism ensures precise sweep gas pressure regulation. Animal experiments utilizing a prototype extracorporeal membrane oxygenation (ECMO) platform validated the integration of online transmembrane pressure identification and adaptive control.</div></div><div><h3>Result:</h3><div>It achieved rapid setpoint tracking with a settling time of less than 4 s and maintained stable transmembrane pressure with a tracking error of less than ±1 mmHg, even during significant blood pressure fluctuations. Blood gas analysis confirmed the system’s efficacy, successfully modulating PaO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> to a target normoxic range (90–200 mmHg) while simultaneously preventing plasma leakage, which was observed at excessive pressure differentials.</div></div><div><h3>Conclusion:</h3><div>This study proposed a novel adaptive control framework for NMP oxygenators,demonstrating a strategy that simultaneously ensures oxygenator safety by preventing plasma leakage and enables therapeutic regulation of PaO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> through on-line model identification, with its clinical potential confirmed in preclinical animal trials. This approach provides a robust foundation for improving organ viability during perfusion and prolonging the functional lifespan of the oxygenator, establishing a new pathway toward safer and more effective organ preservation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109026"},"PeriodicalIF":4.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079699","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":"Automated intravascular ultrasound image processing and quantification of coronary artery anomalies: The AIVUS-CAA software","authors":"Anselm W. Stark ,&nbsp;Pooya Mohammadi Kazaj ,&nbsp;Sebastian Balzer ,&nbsp;Marc Ilic ,&nbsp;Manuel Bergamin ,&nbsp;Ryota Kakizaki ,&nbsp;Andreas Giannopoulos ,&nbsp;Andreas Haeberlin ,&nbsp;Lorenz Räber ,&nbsp;Isaac Shiri ,&nbsp;Christoph Gräni","doi":"10.1016/j.cmpb.2025.109065","DOIUrl":"10.1016/j.cmpb.2025.109065","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Coronary artery anomalies (CAA) with an intramural course are associated with elevated risks of ischemia and sudden cardiac death under stress. Intravascular ultrasound (IVUS) is essential for assessing coronary vessel dynamics in these patients. However, the rarity of such anomalies, along with unique geometric changes in the intramural course and ostium, complicates image analysis, leading to inconsistencies and time-consuming evaluations. Our developed executable, zero/low-code software addresses these limitations by providing automated lumen segmentation and cardiac phase identification in IVUS images acquired during rest and stress protocols.</div></div><div><h3>Methods:</h3><div>The software includes: (1) Automated segmentation of lumen contours trained on 9,418 frames (developed by using human in the loop active learning process) validated on 691 frames and tested on 632 frames, IVUS frames from 76 patients (152 studies) with right CAA using a deep learning (DL) model; (2) Extraction of systolic and diastolic frames via a dual-gating approach combining image- and contour-based methods; and (3) A graphical user interface enabling manual correction of the results. The gating module was validated using a custom flow-loop simulating patient-specific hemodynamics, while segmentation accuracy was assessed via intraclass correlation coefficient (ICC) analysis comparing AI-generated contours with those delineated by experienced readers.</div></div><div><h3>Results:</h3><div>The DL model achieved a mean Dice score of 0.91 (SD: 0.08), sensitivity of 0.95 (SD: 0.12), and specificity of 1.00 (SD: 0.00) on the test set. ICC values for lumen area measurements were 1.00 (95%CI: 1.00–1.00) for rest and 1.00 (95%CI: 1.00–1.00) for stress conditions. The gating module demonstrated excellent reproducibility for identifying systolic and diastolic frames under both conditions (ICC = 1.00 for all).</div></div><div><h3>Conclusions:</h3><div>AIVUS-CAA offers a reliable, automated tool for precise IVUS analysis at rest and during stress, enhancing the evaluation of geometrical changes of coronary vessels in CAA patients and enabling efficient clinical analysis in a streamlined workflow.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109065"},"PeriodicalIF":4.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091394","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":"Assessment of the sheetlet thickness in human left ventricular free wall samples using X-ray phase-contrast microtomography","authors":"Zhaorui Li ,&nbsp;Shunli Wang ,&nbsp;Kai Li ,&nbsp;Huazhan Gui ,&nbsp;Feng Yuan ,&nbsp;Patrick Clarysse ,&nbsp;François Varray","doi":"10.1016/j.cmpb.2025.109058","DOIUrl":"10.1016/j.cmpb.2025.109058","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>In the left ventricular (LV) wall, most cardiomyocytes are organized into sheetlets. A good knowledge of the sheetlet arrangement is crucial for understanding ventricular functions.</div></div><div><h3>Methods:</h3><div>In this paper, we introduced a <em>distance-field</em> method to measure the evolution of the thickness of local sheetlets and cleavage planes (CPs) in the laminar structure regions of five human LV free wall transmural samples. The data were acquired using the European Synchrotron Radiation Facility. The high-resolution synchrotron radiation phase-contrast micro-tomography (SR-PCT) imaging with an isotropic spatial resolution of <span><math><mrow><mn>3</mn><mo>.</mo><mn>5</mn><mo>×</mo><mn>3</mn><mo>.</mo><mn>5</mn><mo>×</mo><mn>3</mn><mo>.</mo><mn>5</mn><mi>μ</mi><msup><mrow><mi>m</mi></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span> allows for a clear observation of the sheetlet arrangement. First, we flattened the samples using Difference of Gaussians (DoG). Secondly, we extracted the sheetlets and CPs using connex filters with different sizes. Then, we generated the laminar structure simulation models to validate <em>distance-field</em> method. Last, we measured the thickness of local CPs and sheetlets by calculating their Chamfer-distance fields.</div></div><div><h3>Results:</h3><div>Sheetlets are thinner and CPs are thicker in the regions with flat sheetlet arrangements; CPs are thicker around blood vessels; and sheetlets are thinner around the intersection region of two sheetlet populations. This regional variation relates to the location of samples in the LV wall, the sheetlet organization manner, and the local myocardial architecture.</div></div><div><h3>Conclusions:</h3><div>The results demonstrate that the distribution of the thickness of sheetlets and CPs is regional, which provides morphology support for the future research about the myocardial mechanical function and the pathological mechanism of heart diseases.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109058"},"PeriodicalIF":4.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085333","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":"Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models","authors":"Paul Calle ,&nbsp;Averi Bates ,&nbsp;Justin C. Reynolds ,&nbsp;Yunlong Liu ,&nbsp;Haoyang Cui ,&nbsp;Sinaro Ly ,&nbsp;Chen Wang ,&nbsp;Qinghao Zhang ,&nbsp;Alberto J. de Armendi ,&nbsp;Shashank S. Shettar ,&nbsp;Kar-Ming Fung ,&nbsp;Qinggong Tang ,&nbsp;Chongle Pan","doi":"10.1016/j.cmpb.2025.109063","DOIUrl":"10.1016/j.cmpb.2025.109063","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>The variability and biases in the real-world performance benchmarking of deep learning models for medical imaging compromise their trustworthiness for real-world deployment. The common approach of holding out a single fixed test set fails to quantify the variance in the estimation of test performance metrics. This study introduces NACHOS (Nested and Automated Cross-validation and Hyperparameter Optimization using Supercomputing) to reduce and quantify the variance of test performance metrics of deep learning models.</div></div><div><h3>Methods:</h3><div>NACHOS integrates Nested Cross-Validation (NCV) and Automated Hyperparameter Optimization (AHPO) within a parallelized high-performance computing (HPC) framework. NACHOS was demonstrated on a chest X-ray repository and an Optical Coherence Tomography (OCT) dataset under multiple data partitioning schemes. Beyond performance estimation, DACHOS (Deployment with Automated Cross-validation and Hyperparameter Optimization using Supercomputing) is introduced to leverage AHPO and cross-validation to build the final model on the full dataset, improving expected deployment performance.</div></div><div><h3>Results:</h3><div>The findings underscore the importance of NCV in quantifying and reducing estimation variance, AHPO in optimizing hyperparameters consistently across test folds, and HPC in ensuring computational feasibility.</div></div><div><h3>Conclusions:</h3><div>By integrating these methodologies, NACHOS and DACHOS provide a scalable, reproducible, and trustworthy framework for DL model evaluation and deployment in medical imaging. To maximize public availability, the full open-source codebase is provided at <span><span>https://github.com/thepanlab/NACHOS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"272 ","pages":"Article 109063"},"PeriodicalIF":4.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046609","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}