Pub Date : 2025-12-18DOI: 10.1016/j.cmpbup.2025.100225
Khaled Al-Thelaya , Nauman Ullah Gilal , Fahad Majeed , Mahmood Alzubaidi , Sabri Boughorbel , William Mifsud , Marco Agus , Jens Schneider
Whole Slide Imaging (WSI) generates vast data sets in histopathology. Manual annotation is impractical and time consuming. There is, thus, a dire need for effective analysis tools. However, a lack of annotated data hampers supervised learning of models that generalize well across domains. Point annotations have emerged as a practical remedy. Motivated by the fact that the randomness of the tissue slice angle and depth renders size measurements of nuclei — such as it would be provided by segmentation — meaningless (unlike in other medical tasks), point annotations are efficient and useful due to their sparseness. In this paper, we formulate the task of nuclei detection as a density estimation problem. We use a U-Net architecture with PoolFormer encoders as the basis to compute point-annotations for nuclei detection. Specifically, we use Gaussian kernels to generate target density masks from a segmented data set and use isocontouring to separate overlapping nuclei. We show that conformal prediction can compute a near-optimal threshold for contouring. This significantly enhances our detection rate. To address cross-domain generalization issues, our framework uses color normalization. As a result, our framework sets a new state-of-the-art in nucleus localization on both the PanNuke and MoNuSeg data sets, and we demonstrate our cross-domain generalization capabilities using samples of the TCGA data set.
{"title":"NuDetect: A point annotation-based framework for nuclei detection using density estimation and conformal thresholding","authors":"Khaled Al-Thelaya , Nauman Ullah Gilal , Fahad Majeed , Mahmood Alzubaidi , Sabri Boughorbel , William Mifsud , Marco Agus , Jens Schneider","doi":"10.1016/j.cmpbup.2025.100225","DOIUrl":"10.1016/j.cmpbup.2025.100225","url":null,"abstract":"<div><div>Whole Slide Imaging (WSI) generates vast data sets in histopathology. Manual annotation is impractical and time consuming. There is, thus, a dire need for effective analysis tools. However, a lack of annotated data hampers supervised learning of models that generalize well across domains. Point annotations have emerged as a practical remedy. Motivated by the fact that the randomness of the tissue slice angle and depth renders size measurements of nuclei — such as it would be provided by segmentation — meaningless (unlike in other medical tasks), point annotations are efficient and useful due to their sparseness. In this paper, we formulate the task of nuclei detection as a density estimation problem. We use a U-Net architecture with PoolFormer encoders as the basis to compute point-annotations for nuclei detection. Specifically, we use Gaussian kernels to generate target density masks from a segmented data set and use isocontouring to separate overlapping nuclei. We show that conformal prediction can compute a near-optimal threshold for contouring. This significantly enhances our detection rate. To address cross-domain generalization issues, our framework uses color normalization. As a result, our framework sets a new state-of-the-art in nucleus localization on both the PanNuke and MoNuSeg data sets, and we demonstrate our cross-domain generalization capabilities using samples of the TCGA data set.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.cmpbup.2025.100224
Katja Löwenstein , Johanna Rehrl , Anja Schuster , Michael Gadermayr
The in vitro scratch assay is a widely used assay in cell biology to assess the rate of wound closure related to a variety of therapeutic interventions. While manual measurement is subjective and vulnerable to intra- and interobserver variability, computer-based tools are theoretically objective, but in practice often contain parameters which are manually adjusted (individually per image or data set) and thereby provide a source for subjectivity. Modern deep learning approaches typically require large annotated training data which complicates instant applicability. In this paper, we deeply investigate the Segment Anything Model (SAM), a deep foundation model based on interactive point-prompts, which enables class-agnostic segmentation without tuning the network’s parameters based on any domain specific training data. With respect to segmentation accuracy, the interactive method significantly outperformed a semi-objective baseline that required manual inspection and, when necessary, parameter adjustments for each image. Experiments were conducted to evaluate the impact of variability due to interactive prompting. The results exhibited remarkably low intra- and interobserver variability, clearly surpassing the consistency of manual segmentation by domain experts. In addition, a fully automated zero-shot approach was explored, incorporating the self-supervised learning model DINOv2 as a preprocessing step before sampling input points for SAM, with various sampling methods systematically investigated.
{"title":"Towards objective In-Vitro wound healing assessment with segment anything: A large evaluation of interactive and automated pipelines","authors":"Katja Löwenstein , Johanna Rehrl , Anja Schuster , Michael Gadermayr","doi":"10.1016/j.cmpbup.2025.100224","DOIUrl":"10.1016/j.cmpbup.2025.100224","url":null,"abstract":"<div><div>The <em>in vitro</em> scratch assay is a widely used assay in cell biology to assess the rate of wound closure related to a variety of therapeutic interventions. While manual measurement is subjective and vulnerable to intra- and interobserver variability, computer-based tools are theoretically objective, but in practice often contain parameters which are manually adjusted (individually per image or data set) and thereby provide a source for subjectivity. Modern deep learning approaches typically require large annotated training data which complicates instant applicability. In this paper, we deeply investigate the Segment Anything Model (SAM), a deep foundation model based on interactive point-prompts, which enables class-agnostic segmentation without tuning the network’s parameters based on any domain specific training data. With respect to segmentation accuracy, the interactive method significantly outperformed a semi-objective baseline that required manual inspection and, when necessary, parameter adjustments for each image. Experiments were conducted to evaluate the impact of variability due to interactive prompting. The results exhibited remarkably low intra- and interobserver variability, clearly surpassing the consistency of manual segmentation by domain experts. In addition, a fully automated zero-shot approach was explored, incorporating the self-supervised learning model DINOv2 as a preprocessing step before sampling input points for SAM, with various sampling methods systematically investigated.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oropharynx Squamous Cell Carcinoma (OPSCC) linked to Human Papillomavirus (HPV) exhibits a more favorable prognosis than other squamous cell carcinomas of the upper aerodigestive tract. Finding reliable non-invasive detection methods of this prognostic entity is key to propose appropriate therapeutic decisions. This study aims to provide a comprehensive method based on pre-treatment clinical data for predicting the patient’s HPV status over a large OPSCC patient cohort and employing explainability techniques to interpret the significance and effects of the features.
Materials and Methods:
We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. For explainability, SHAP and LIME were used to identify the most relevant relationships and their effect on the predictive model. Furthermore, additional publicly available datasets were scrutinized to compare outcomes and assess the method’s generalization across diverse feature sets and populations.
Results:
The best model yielded an AUC of 0.85, a sensitivity of 0.83, and a specificity of 0.75 over the testing set. The explainability analysis highlighted the remarkable significance of specific clinical attributes, in particular the oropharynx subsite tumor location and the patient’s smoking history. The contribution of each variable to the prediction was substantiated by creating a 95% confidence intervals of model coefficients by means of a 10,000 sample bootstrap and by analyzing top contributors across the best-performing models.
Conclusions:
The combination of specific clinical factors typically collected for OPSCC patients, such as smoking habits and the tumor oropharynx sub-location, along with the ML models hereby presented, can by themselves provide an informed analysis of the HPV status, and of proper use of data science techniques to explain it. Future work should focus on adding other data modalities such as CT scans to enhance performance and to uncover new relations, thus aiding medical practitioners in diagnosing OPSCC more accurately.
{"title":"Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability","authors":"Emily Diaz Badilla , Ignasi Cos , Claudio Sampieri , Berta Alegre , Isabel Vilaseca , Simone Balocco , Petia Radeva","doi":"10.1016/j.cmpbup.2024.100170","DOIUrl":"10.1016/j.cmpbup.2024.100170","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Oropharynx Squamous Cell Carcinoma (OPSCC) linked to Human Papillomavirus (HPV) exhibits a more favorable prognosis than other squamous cell carcinomas of the upper aerodigestive tract. Finding reliable non-invasive detection methods of this prognostic entity is key to propose appropriate therapeutic decisions. This study aims to provide a comprehensive method based on pre-treatment clinical data for predicting the patient’s HPV status over a large OPSCC patient cohort and employing explainability techniques to interpret the significance and effects of the features.</div></div><div><h3>Materials and Methods:</h3><div>We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. For explainability, SHAP and LIME were used to identify the most relevant relationships and their effect on the predictive model. Furthermore, additional publicly available datasets were scrutinized to compare outcomes and assess the method’s generalization across diverse feature sets and populations.</div></div><div><h3>Results:</h3><div>The best model yielded an AUC of 0.85, a sensitivity of 0.83, and a specificity of 0.75 over the testing set. The explainability analysis highlighted the remarkable significance of specific clinical attributes, in particular the oropharynx subsite tumor location and the patient’s smoking history. The contribution of each variable to the prediction was substantiated by creating a 95% confidence intervals of model coefficients by means of a 10,000 sample bootstrap and by analyzing top contributors across the best-performing models.</div></div><div><h3>Conclusions:</h3><div>The combination of specific clinical factors typically collected for OPSCC patients, such as smoking habits and the tumor oropharynx sub-location, along with the ML models hereby presented, can by themselves provide an informed analysis of the HPV status, and of proper use of data science techniques to explain it. Future work should focus on adding other data modalities such as CT scans to enhance performance and to uncover new relations, thus aiding medical practitioners in diagnosing OPSCC more accurately.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100170"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100180
A Z M Ehtesham Chowdhury , Andrew Mehnert , Graham Mann , William H. Morgan , Ferdous Sohel
Optic disc (OD) segmentation from retinal images is crucial for diagnosing, assessing, and tracking the progression of several sight-threatening diseases. This paper presents a deep machine-learning method for semantically segmenting OD from retinal images. The method is named multiscale guided attention network (MSGANet-OD), comprising encoders for extracting multiscale features and decoders for constructing segmentation maps from the extracted features. The decoder also includes a guided attention module that incorporates features related to structural, contextual, and illumination information to segment OD. A custom loss function is proposed to retain the optic disc's geometrical shape (i.e., elliptical) constraint and to alleviate the blood vessels' influence in the overlapping region between the OD and vessels. MSGANet-OD was trained and tested on an in-house clinical color retinal image dataset captured during ophthalmodynamometry as well as on several publicly available color fundus image datasets, e.g., DRISHTI-GS, RIM-ONE-r3, and REFUGE1. Experimental results show that MSGANet-OD achieved superior OD segmentation performance from ophthalmodynamometry images compared to widely used segmentation methods. Our method also achieved competitive results compared to state-of-the-art OD segmentation methods on public datasets. The proposed method can be used in automated systems to quantitatively assess optic nerve head abnormalities (e.g., glaucoma, optic disc neuropathy) and vascular changes in the OD region.
{"title":"Multiscale guided attention network for optic disc segmentation of retinal images","authors":"A Z M Ehtesham Chowdhury , Andrew Mehnert , Graham Mann , William H. Morgan , Ferdous Sohel","doi":"10.1016/j.cmpbup.2025.100180","DOIUrl":"10.1016/j.cmpbup.2025.100180","url":null,"abstract":"<div><div>Optic disc (OD) segmentation from retinal images is crucial for diagnosing, assessing, and tracking the progression of several sight-threatening diseases. This paper presents a deep machine-learning method for semantically segmenting OD from retinal images. The method is named multiscale guided attention network (MSGANet-OD), comprising encoders for extracting multiscale features and decoders for constructing segmentation maps from the extracted features. The decoder also includes a guided attention module that incorporates features related to structural, contextual, and illumination information to segment OD. A custom loss function is proposed to retain the optic disc's geometrical shape (i.e., elliptical) constraint and to alleviate the blood vessels' influence in the overlapping region between the OD and vessels. MSGANet-OD was trained and tested on an in-house clinical color retinal image dataset captured during ophthalmodynamometry as well as on several publicly available color fundus image datasets, e.g., DRISHTI-GS, RIM-ONE-r3, and REFUGE1. Experimental results show that MSGANet-OD achieved superior OD segmentation performance from ophthalmodynamometry images compared to widely used segmentation methods. Our method also achieved competitive results compared to state-of-the-art OD segmentation methods on public datasets. The proposed method can be used in automated systems to quantitatively assess optic nerve head abnormalities (e.g., glaucoma, optic disc neuropathy) and vascular changes in the OD region.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100196
Noemi Giordano, Silvia Cannone, Gabriella Balestra, Marco Knaflitz
Goal
The home monitoring of cardiac time intervals reduces hospitalization and mortality of cardiovascular patients. However, a reliable time reference in the electrocardiogram is necessary. Nevertheless, the use of different single leads, typical of wearable devices, impacts the repeatability of the time reference and thus the accuracy of the time-dependent parameters. This work proposes a simple approach to detect the peak and onset of the ventricular depolarization, and demonstrates its lead independence, which makes it suitable for wearable devices even with non-standard leads.
Methods
Our method grounds on an energy-based approach, which we applied on a) a publicly available dataset with standard 12-lead recordings; b) a proof-of-concept dataset including a custom precordial non-standard lead implemented on a wearable device.
Results
Compared against the Pan-Tompkins algorithm, our method reduced the absolute error between each lead and the first standard lead by 26 % to 64 % for the peak, and by 70 % to 82 % for the onset detection. The achieved consistency across leads is compatible with clinical monitoring. The computational time was also reduced by 65 % to 96 %, making the algorithm suitable for use on microcontroller-based wearable devices.
Conclusions
The proposed method enables the identification of a stable reference of the ventricular depolarization regardless of the choice of the lead. The presented results open to the implementation on wearable devices for chronic disease monitoring purposes.
{"title":"Independence on the lead of the identification of the ventricular depolarization in the electrocardiogram in wearable devices","authors":"Noemi Giordano, Silvia Cannone, Gabriella Balestra, Marco Knaflitz","doi":"10.1016/j.cmpbup.2025.100196","DOIUrl":"10.1016/j.cmpbup.2025.100196","url":null,"abstract":"<div><h3>Goal</h3><div>The home monitoring of cardiac time intervals reduces hospitalization and mortality of cardiovascular patients. However, a reliable time reference in the electrocardiogram is necessary. Nevertheless, the use of different single leads, typical of wearable devices, impacts the repeatability of the time reference and thus the accuracy of the time-dependent parameters. This work proposes a simple approach to detect the peak and onset of the ventricular depolarization, and demonstrates its lead independence, which makes it suitable for wearable devices even with non-standard leads.</div></div><div><h3>Methods</h3><div>Our method grounds on an energy-based approach, which we applied on a) a publicly available dataset with standard 12-lead recordings; b) a proof-of-concept dataset including a custom precordial non-standard lead implemented on a wearable device.</div></div><div><h3>Results</h3><div>Compared against the Pan-Tompkins algorithm, our method reduced the absolute error between each lead and the first standard lead by 26 % to 64 % for the peak, and by 70 % to 82 % for the onset detection. The achieved consistency across leads is compatible with clinical monitoring. The computational time was also reduced by 65 % to 96 %, making the algorithm suitable for use on microcontroller-based wearable devices.</div></div><div><h3>Conclusions</h3><div>The proposed method enables the identification of a stable reference of the ventricular depolarization regardless of the choice of the lead. The presented results open to the implementation on wearable devices for chronic disease monitoring purposes.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the finite-time stability of fractional-order (FO) discrete Susceptible–Infected–Recovered (SIR) models for COVID-19, incorporating memory effects to capture real-world epidemic dynamics. We use discrete fractional calculus to analyze the stability of disease-free and pandemic equilibrium points. The theoretical framework introduces essential definitions, finite-time stability (FTS) criteria, and novel fractional-order modeling insights. Numerical simulations validate the theoretical results under various parameters, demonstrating the finite-time convergence to equilibrium states. Results highlight the flexibility of FO models in addressing delayed responses and prolonged effects, offering enhanced predictive accuracy over traditional integer-order approaches. This research contributes to the design of effective public health interventions and advances in mathematical epidemiology.
{"title":"On finite-time stability of some COVID-19 models using fractional discrete calculus","authors":"Shaher Momani , Iqbal M. Batiha , Issam Bendib , Abeer Al-Nana , Adel Ouannas , Mohamed Dalah","doi":"10.1016/j.cmpbup.2025.100188","DOIUrl":"10.1016/j.cmpbup.2025.100188","url":null,"abstract":"<div><div>This study investigates the finite-time stability of fractional-order (FO) discrete Susceptible–Infected–Recovered (SIR) models for COVID-19, incorporating memory effects to capture real-world epidemic dynamics. We use discrete fractional calculus to analyze the stability of disease-free and pandemic equilibrium points. The theoretical framework introduces essential definitions, finite-time stability (FTS) criteria, and novel fractional-order modeling insights. Numerical simulations validate the theoretical results under various parameters, demonstrating the finite-time convergence to equilibrium states. Results highlight the flexibility of FO models in addressing delayed responses and prolonged effects, offering enhanced predictive accuracy over traditional integer-order approaches. This research contributes to the design of effective public health interventions and advances in mathematical epidemiology.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visualization of virtual resections plays a central role in computer-assisted liver surgery planning. However, the intricate liver anatomical information often results in occlusions and visualization information clutter, which can lead to inaccuracies in virtual resections. To overcome these challenges, we introduce Resectograms, which are planar (2D) representations of virtual resections enabling the visualization of information associated with the surgical plan.
Methods:
Resectograms are computed in real-time and displayed as additional 2D views showing anatomical, functional, and risk-associated information extracted from the 3D virtual resection as this is modified during planning, offering surgeons an occlusion-free visualization of the virtual resection during surgery planning. To further improve functionality, we explored three flattening methods: fixed-shape, Least Squares Conformal Maps, and As-Rigid-As-Possible, to generate these 2D views. Additionally, we optimized GPU memory usage by downsampling texture objects, ensuring errors remain within acceptable limits as defined by surgeons.
Results:
We evaluated Resectograms with experienced surgeons (n = 4, 9-15 years) and assessed 2D flattening methods with computer and biomedical scientists (n = 11) through visual experiments. Surgeons found Resectograms valuable for enhancing surgical planning effectiveness and accuracy. Among flattening methods, Least Squares Conformal Maps and As-Rigid-As-Possible techniques demonstrated similarly low distortion levels, superior to the fixed-shape approach. Our analysis of texture object downsampling revealed effectiveness for liver and tumor segmentations, but less so for vessel segmentations.
Conclusions:
This paper presents Resectograms, a novel method for visualizing liver virtual resection plans in 2D, offering an intuitive, occlusion-free representation computable in real-time. Resectograms incorporate multiple information layers, providing comprehensive data for liver surgery planning. We enhanced the visualization through improved 3D-to-2D orientation mapping and distortion-minimizing parameterization algorithms. This research contributes to advancing liver surgery planning tools by offering a more accessible and informative visualization method. The code repository for this work is available at: https://github.com/ALive-research/Slicer-Liver.
{"title":"Resectograms: Planning liver surgery with real-time occlusion-free visualization of virtual resections","authors":"Ruoyan Meng , Davit Aghayan , Egidijus Pelanis , Bjørn Edwin , Faouzi Alaya Cheikh , Rafael Palomar","doi":"10.1016/j.cmpbup.2025.100186","DOIUrl":"10.1016/j.cmpbup.2025.100186","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Visualization of virtual resections plays a central role in computer-assisted liver surgery planning. However, the intricate liver anatomical information often results in occlusions and visualization information clutter, which can lead to inaccuracies in virtual resections. To overcome these challenges, we introduce <em>Resectograms</em>, which are planar (2D) representations of virtual resections enabling the visualization of information associated with the surgical plan.</div></div><div><h3>Methods:</h3><div>Resectograms are computed in real-time and displayed as additional 2D views showing anatomical, functional, and risk-associated information extracted from the 3D virtual resection as this is modified during planning, offering surgeons an occlusion-free visualization of the virtual resection during surgery planning. To further improve functionality, we explored three flattening methods: fixed-shape, Least Squares Conformal Maps, and As-Rigid-As-Possible, to generate these 2D views. Additionally, we optimized GPU memory usage by downsampling texture objects, ensuring errors remain within acceptable limits as defined by surgeons.</div></div><div><h3>Results:</h3><div>We evaluated Resectograms with experienced surgeons (n = 4, 9-15 years) and assessed 2D flattening methods with computer and biomedical scientists (n = 11) through visual experiments. Surgeons found Resectograms valuable for enhancing surgical planning effectiveness and accuracy. Among flattening methods, Least Squares Conformal Maps and As-Rigid-As-Possible techniques demonstrated similarly low distortion levels, superior to the fixed-shape approach. Our analysis of texture object downsampling revealed effectiveness for liver and tumor segmentations, but less so for vessel segmentations.</div></div><div><h3>Conclusions:</h3><div>This paper presents Resectograms, a novel method for visualizing liver virtual resection plans in 2D, offering an intuitive, occlusion-free representation computable in real-time. Resectograms incorporate multiple information layers, providing comprehensive data for liver surgery planning. We enhanced the visualization through improved 3D-to-2D orientation mapping and distortion-minimizing parameterization algorithms. This research contributes to advancing liver surgery planning tools by offering a more accessible and informative visualization method. The code repository for this work is available at: <span><span>https://github.com/ALive-research/Slicer-Liver</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100182
Deepak Kumar , Chaman Verma , Zoltán Illés
Background and Objective:
Early detection of eye diseases, especially cataracts, is essential for preventing vision impairment. Accurate and cost-effective cataract diagnosis often requires advanced methods. This study proposes novel deep learning models that integrate global and local attention mechanisms into MobileNet and InceptionV3 architectures to improve cataract detection from fundus images.
Methods:
Two deep learning models, Global–Local Attention Augmented MobileNet (GLAAM) and Global–Local Attention Augmented InceptionV3 (GLAAI), were developed to enhance the analysis of fundus images. The models incorporate a combined attention mechanism to effectively capture deteriorated regions in retinal images. Data augmentation techniques were employed to prevent overfitting during training and testing on two cataract datasets. Additionally, Grad-CAM visualizations were used to increase interpretability by highlighting key regions influencing predictions.
Results:
The GLAAM model achieved a balanced accuracy of 97.08%, an average precision of 97.11%, and an F1-score of 97.12% on the retinal dataset. Grad-CAM visualizations confirmed the models’ ability to identify crucial cataract-related regions in fundus images.
Conclusion:
This study demonstrates a significant advancement in cataract diagnosis using deep learning, with GLAAM and GLAAI models exhibiting strong diagnostic performance. These models have the potential to enhance diagnostic tools and improve patient care by offering a cost-effective and accurate solution for cataract detection, suitable for integration into clinical settings.
{"title":"GLAAM and GLAAI: Pioneering attention models for robust automated cataract detection","authors":"Deepak Kumar , Chaman Verma , Zoltán Illés","doi":"10.1016/j.cmpbup.2025.100182","DOIUrl":"10.1016/j.cmpbup.2025.100182","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Early detection of eye diseases, especially cataracts, is essential for preventing vision impairment. Accurate and cost-effective cataract diagnosis often requires advanced methods. This study proposes novel deep learning models that integrate global and local attention mechanisms into MobileNet and InceptionV3 architectures to improve cataract detection from fundus images.</div></div><div><h3>Methods:</h3><div>Two deep learning models, Global–Local Attention Augmented MobileNet (GLAAM) and Global–Local Attention Augmented InceptionV3 (GLAAI), were developed to enhance the analysis of fundus images. The models incorporate a combined attention mechanism to effectively capture deteriorated regions in retinal images. Data augmentation techniques were employed to prevent overfitting during training and testing on two cataract datasets. Additionally, Grad-CAM visualizations were used to increase interpretability by highlighting key regions influencing predictions.</div></div><div><h3>Results:</h3><div>The GLAAM model achieved a balanced accuracy of 97.08%, an average precision of 97.11%, and an F1-score of 97.12% on the retinal dataset. Grad-CAM visualizations confirmed the models’ ability to identify crucial cataract-related regions in fundus images.</div></div><div><h3>Conclusion:</h3><div>This study demonstrates a significant advancement in cataract diagnosis using deep learning, with GLAAM and GLAAI models exhibiting strong diagnostic performance. These models have the potential to enhance diagnostic tools and improve patient care by offering a cost-effective and accurate solution for cataract detection, suitable for integration into clinical settings.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100191
Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri
Background
The issue is that most heart attacks and strokes happen unexpectedly to people who have signs of high blood pressure that are not identified in time for treatment. These gap factors make the research on hypertensive retinopathy urgent since it requires an early detection model to improve treatment accuracy and prevent heart attacks and strokes before they happen.
Methods
This research utilizes secondary data, specifically a retinal image dataset from the open-source Messidor database. This database comprises 1200 retinal images, each measuring 1440 × 940 pixels. The dataset is divided into 60 % training and 40 % validation data. The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. A Morphological Approach is used to obtain comprehensive features of the blood vessels around the Optic Disc (OD). This stage aims to extract and sample the comparison between the width of the artery and vein (AVR). This research uses a Deep Convolutional Neural Network (DCNN) classification model with cross-validation training using the Leave-one-out method.
Results
The results of testing the model with nine output classes, the features extracted in each convolutional layer, the second layer successfully extracts the retina and eye blood vessels, the third layer extracts the retinal image texture, and the fourth layer extracts hard exudates, hemorrhages, and cotton wool spots. Meanwhile, the Specificity, Recall, Accuracy, and F-Score results are 90 %, 81.82 %, 90 %, and 90 %, respectively.
Conclusions
This research's findings first include applying the AVR calculation algorithm to build a new dataset with 9 class categories. Second, the architectural specifications of the CNN model are determined, and the input size, depth, and number of nodes for each layer, as well as the transfer function, learning rate, and number of epochs, are set by adjusting hyperparameters.
{"title":"Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach","authors":"Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri","doi":"10.1016/j.cmpbup.2025.100191","DOIUrl":"10.1016/j.cmpbup.2025.100191","url":null,"abstract":"<div><h3>Background</h3><div>The issue is that most heart attacks and strokes happen unexpectedly to people who have signs of high blood pressure that are not identified in time for treatment. These gap factors make the research on hypertensive retinopathy urgent since it requires an early detection model to improve treatment accuracy and prevent heart attacks and strokes before they happen.</div></div><div><h3>Methods</h3><div>This research utilizes secondary data, specifically a retinal image dataset from the open-source Messidor database. This database comprises 1200 retinal images, each measuring 1440 × 940 pixels. The dataset is divided into 60 % training and 40 % validation data. The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. A Morphological Approach is used to obtain comprehensive features of the blood vessels around the Optic Disc (OD). This stage aims to extract and sample the comparison between the width of the artery and vein (AVR). This research uses a Deep Convolutional Neural Network (DCNN) classification model with cross-validation training using the Leave-one-out method.</div></div><div><h3>Results</h3><div>The results of testing the model with nine output classes, the features extracted in each convolutional layer, the second layer successfully extracts the retina and eye blood vessels, the third layer extracts the retinal image texture, and the fourth layer extracts hard exudates, hemorrhages, and cotton wool spots. Meanwhile, the Specificity, Recall, Accuracy, and F-Score results are 90 %, 81.82 %, 90 %, and 90 %, respectively.</div></div><div><h3>Conclusions</h3><div>This research's findings first include applying the AVR calculation algorithm to build a new dataset with 9 class categories. Second, the architectural specifications of the CNN model are determined, and the input size, depth, and number of nodes for each layer, as well as the transfer function, learning rate, and number of epochs, are set by adjusting hyperparameters.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100195
Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai
Objectives
This study presents an improved approach to person identification (PID) using nonverbal vocalizations, focusing specifically on cough sounds as a biometric modality. While recent works have demonstrated the feasibility of cough-based PID (CPID), most report accuracies around 80–90 % and could face limitations in terms of model efficiency, generalization, or robustness. Our objective is to advance CPID performance through compact model design and more effective training strategies.
Methods
We collected a custom dataset from 19 subjects and developed a lightweight yet effective deep learning framework for CPID. The proposed architecture, CoughCueNet, is a convolutional recurrent neural network designed to capture both spatial and temporal patterns in cough sounds. The training process incorporates a hybrid loss function that combines supervised contrastive (SC) learning and cross-entropy (CE) loss to enhance feature discrimination. We systematically evaluated multiple acoustic representations, including MFCCs and spectrograms, to identify the most suitable features. We also applied data augmentation for robustness and investigated cross-modal transferability by testing speech-trained models on cough data.
Results
Our CPID system achieved a mean identification accuracy of 97.18 %. Training the proposed CoughCueNet using a hybrid SC+CE loss function consistently improved model generalization and robustness. It outperformed the same network and larger-capacity networks (i.e., VGG16 and ResNet50) trained with CE loss alone, which achieved accuracies around 90 %. Among the tested features, MFCCs yielded superior identification performance over spectrograms. Experiments with speech-trained models tested on coughs revealed limited cross-vocal transferability, emphasizing the need for cough-specific models.
Conclusion
This work advances the state of cough-based PID by demonstrating that high-accuracy identification is achievable using compact models and hybrid training strategies. It establishes cough sounds as a practical and distinctive biometric modality, with promising applications in security, user authentication, and health monitoring, particularly in environments where speech-based systems are less reliable or infeasible.
{"title":"Acoustic cues for person identification using cough sounds","authors":"Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai","doi":"10.1016/j.cmpbup.2025.100195","DOIUrl":"10.1016/j.cmpbup.2025.100195","url":null,"abstract":"<div><h3>Objectives</h3><div>This study presents an improved approach to person identification (PID) using nonverbal vocalizations, focusing specifically on cough sounds as a biometric modality. While recent works have demonstrated the feasibility of cough-based PID (CPID), most report accuracies around 80–90 % and could face limitations in terms of model efficiency, generalization, or robustness. Our objective is to advance CPID performance through compact model design and more effective training strategies.</div></div><div><h3>Methods</h3><div>We collected a custom dataset from 19 subjects and developed a lightweight yet effective deep learning framework for CPID. The proposed architecture, CoughCueNet, is a convolutional recurrent neural network designed to capture both spatial and temporal patterns in cough sounds. The training process incorporates a hybrid loss function that combines supervised contrastive (SC) learning and cross-entropy (CE) loss to enhance feature discrimination. We systematically evaluated multiple acoustic representations, including MFCCs and spectrograms, to identify the most suitable features. We also applied data augmentation for robustness and investigated cross-modal transferability by testing speech-trained models on cough data.</div></div><div><h3>Results</h3><div>Our CPID system achieved a mean identification accuracy of 97.18 %. Training the proposed CoughCueNet using a hybrid SC+CE loss function consistently improved model generalization and robustness. It outperformed the same network and larger-capacity networks (i.e., VGG16 and ResNet50) trained with CE loss alone, which achieved accuracies around 90 %. Among the tested features, MFCCs yielded superior identification performance over spectrograms. Experiments with speech-trained models tested on coughs revealed limited cross-vocal transferability, emphasizing the need for cough-specific models.</div></div><div><h3>Conclusion</h3><div>This work advances the state of cough-based PID by demonstrating that high-accuracy identification is achievable using compact models and hybrid training strategies. It establishes cough sounds as a practical and distinctive biometric modality, with promising applications in security, user authentication, and health monitoring, particularly in environments where speech-based systems are less reliable or infeasible.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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