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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Samir, Jessica Ramadass, Karthik Saunders, Adam M. Krishnan, Aravind R. Remedios, Lucas W. McMaster, Elyssa M. Landman, Bennett A. Dept. of Psychological Science and Neuroscience Belmont University NashvilleTN United States Dept. of Computer Science NashvilleTN United States Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center NashvilleTN United States

ISBN: (纸本)9781510685987

Segmentation of anatomical structures leverages medical imaging technology for accurate diagnosis and treatment planning. Recent advances in active learning offer the opportunity for the interactive creation of artificial intelligence (AI) segmentation algorithms. Although active learning has been extensively investigated retrospectively, prospective studies have not been widely reported. This study aimed to determine whether a novice in medical imaging could independently label anatomical structures using Radiopaedia, 20% of clinician-generated labels for education of the novice, and AI guidance. Given the pancreas and spleen challenges from the Medical Segmentation Decathlon (MSD), a novice employed the MONAI Label extension on 3D Slicer with active learning (AL) techniques to perform segmentation. This study also explored the use of MONAI (Medical Open Network for AI) Label, a user-friendly framework for label submission and model training. Our segmentation results yielded Dice scores comparable to the range of those from the 2018 segmentation challenges of the MSD. We achieved Dice scores of 0.209 for the pancreas task using 70 images and 0.831 for the spleen using 34 images. The pancreas results do not fall within MSD’s reported ranges for the L1 region which is 0.48-0.80. However, the spleen results fall within the MSD’s reported range of 0.83-0.97. The interactive AI framework by MONAI Label effectively segments as we reached reasonable performance in less than a month with one person without clinical expertise. © 2025 SPIE.

关键词： Medical education

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Measuring Motor Planning Using an Affordable Sound-Based Virtual Reality Setup for Accessible Perception-Action Studies

Measuring Motor Planning Using an Affordable Sound-Based Vir...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Roberto Barumerli Alessandro Giuseppe Privitera Alessandro Carollo Giovanni Fontana Eros Patarini Alberto Zaninelli Paola Cesari Michele Geronazzo Dept. of Neuroscience Biomedicine and Movement University of Verona (IT) Dept. Mathematics Computer Science and Physics University of Udine (IT) Dept. of Engineering and Management University of Padova (IT)

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

The recent availability of consumer head-mounted displays (HMDs) for virtual reality (VR) provides an immersive and affordable platform to study sound perception and action outside laboratory settings. While traditional perception-action studies rely on complex systems like motion capture, HMDs integrate dynamic sound delivery and real-time movement tracking. In this study, we adapted the experimental protocol from Geronazzo et al. (2023) to test the feasibility of measuring user behaviour with a consumer-grade HMD. Instead of muscular activity measured from surface electromyography, we analysed reaction times using kinematic data from the HMD’s onboard sensors and controllers. Data from ten participants showed that spatial properties of looming sounds influence auditory perception-action loops, aligning with findings from the original study. Our results demonstrate that reaction time is a scalable and meaningful measure for studying space perception through sound, providing a possible alternative to muscular activity measurements and showcasing the potential of consumer-grade VR for accessible and robust experimental research despite current limitations.

关键词： Three-dimensional displays Atmospheric measurements Tracking Current measurement Virtual reality Resists User interfaces Particle measurements Time measurement Sensors

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Measuring Motor Planning Using an Affordable Sound-Based Virtual Reality Setup for Accessible Perception-Action Studies

Measuring Motor Planning Using an Affordable Sound-Based Vir...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Barumerli, Roberto Privitera, Alessandro Giuseppe Carollo, Alessandro Fontana, Giovanni Patarini, Eros Zaninelli, Alberto Cesari, Paola Geronazzo, Michele University of Verona (IT) Dept. of Neuroscience Biomedicine and Movement Italy University of Udine (IT) Dept. Mathematics Computer Science and Physics Italy University of Padova (IT) Dept. of Engineering and Management Italy

ISBN: (纸本)9798331514846

The recent availability of consumer head-mounted displays (HMDs) for virtual reality (VR) provides an immersive and affordable platform to study sound perception and action outside laboratory settings. While traditional perception-action studies rely on complex systems like motion capture, HMDs integrate dynamic sound delivery and real-time movement tracking. In this study, we adapted the experimental protocol from Geronazzo et al. (2023) to test the feasibility of measuring user behaviour with a consumer-grade HMD. Instead of muscular activity measured from surface electromyography, we analysed reaction times using kinematic data from the HMD's onboard sensors and controllers. Data from ten participants showed that spatial properties of looming sounds influence auditory perception-action loops, aligning with findings from the original study. Our results demonstrate that reaction time is a scalable and meaningful measure for studying space perception through sound, providing a possible alternative to muscular activity measurements and showcasing the potential of consumer-grade VR for accessible and robust experimental research despite current limitations. © 2025 IEEE.

关键词： auditory perception consumer devices looming sounds portable setup reaction time

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A Parallel JPEG Pleno Baseline Block-Based Profile Light Field Encoder Using OpenMP

A Parallel JPEG Pleno Baseline Block-Based Profile Light Fie...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Ismael Seidel André Filipe da Silva Fernandes José Luis Güntzel Dept. of Computer Science and Statistics (INE) Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

Light Fields (LFs) are a plenoptic image modality that provides more information on light rays, making them an excellent representation for immersive media. To compress such a modality, the Joint Photographic Experts Group (JPEG) committee created the JPEG Pleno Part 2 standard with two profiles. This work focuses on the reference encoder implementation for the Baseline Block-Based Profile (BBBP), called JPEG Pleno Model (JPLM). Our main contribution lies in the proposal and analysis of a parallel implementation of JPLM using OpenMP. We show that it is possible to accelerate encoding from nearly 2 to 10 times when using 2 to 16 threads, with a memory overhead ranging from 15% up to 78%, depending on the LF size. Moreover, the speedup comes with no cost in terms of coding efficiency, i.e., the LFs encoded with the proposed parallel version are bit-exact matches to ones encoded with the sequential version.

关键词： Costs Instruction sets Transform coding Transforms Encoding Light fields Software Decoding Proposals Standards

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Bundle-wise functional connectivity density and fractional amplitude of low-frequency fluctuations decrease in white matter in preclinical Alzheimer’s disease and are associated with Aβ levels and cognition

Bundle-wise functional connectivity density and fractional a...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Chang, Yukie Xu, Lyuan Gao, Chenyu Khairi, Nazirah Mohd Gore, John C. Landman, Bennett A. Gao, Yurui Dept. of Computer Science Pomona College 333 N College Way ClaremontCA91711 United States Dept. of Neuroscience Pomona College 333 N College Way ClaremontCA91711 United States Dept. of Electrical and Computer Engineering Vanderbilt Univ. 2301 Vanderbilt Place NashvilleTN37235 United States VUMC 1161 21st Ave. NashvilleTN37232 United States Dept. of Radiology and Radiological Sciences VUMC 1161 21st Ave. NashvilleTN37232 United States Dept. of Biomedical Engineering Vanderbilt Univ. 2301 Vanderbilt Place NashvilleTN37235 United States

ISBN: (纸本)9781510685987

Neurophysiological changes associated with Alzheimer’s disease (AD) begin decades before clinical symptoms emerge, during preclinical AD. Functional abnormalities in white matter (WM) at this preclinical stage remain largely unexplored. We obtained resting-state functional magnetic resonance imaging (rs-fMRI) data of 295 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and evaluated bundle-wise functional connectivity density (FCD) and fractional amplitude of low-frequency fluctuations (fALFF) across 46 bundles, which reflects the strength of synchronizations of BOLD dynamics between each WM bundle and whole-brain 200 GM parcels, and spontaneous neural activity of each WM bundle, respectively. To mitigate site/scanner effects on the metrics, ComBat harmonization was applied to the data. We then performed permutation tests (n=5,000) on each harmonized metric for each bundle to determine differences in FCD and fALFF in preclinical AD relative to controls, adjusting for sex, age, and education using multiple linear regression. Linear correlations of the metrics with the pathological biomarker beta-amyloid (Aβ) and cognitive scores (mPACC and ADAS11) were assessed using general linear models. Multiple comparisons were corrected via a false discovery rate (FDR). We found that preclinical AD patients had reduced FCD and fALFF in specific WM bundles, such as cingulate and hippocampal cingulum, compared to controls (FDR corrected p © 2025 SPIE.

关键词： Brain mapping

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The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline

arXiv

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arXiv 2025年

作者： Civitarese, Gabriele Fiori, Michele Arighi, Andrea Galimberti, Daniela Florio, Graziana Bettini, Claudio EveryWare Lab Dept. of Computer Science University of Milan Milan Italy Dept. of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Dept. of Neuroscience and Mental Health Area Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan Italy

Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI patients exhibit cognitive dysfunctions without significant daily functional loss. While MCI may progress to dementia, predicting this transition remains a clinical challenge due to limited and unreliable indicators. Behavioral changes, like in the execution of Activities of Daily Living (ADLs), can signal such progression. Sensorized smart homes and wearable devices offer an innovative solution for continuous, non-intrusive monitoring ADLs for MCI patients. However, current machine learning models for detecting behavioral changes lack transparency, hindering clinicians’ trust. This paper introduces the SERENADE project, a European Union-funded initiative that aims to detect and explain behavioral changes associated with cognitive decline using explainable AI methods. SERENADE aims at collecting one year of data from 30 MCI patients living alone, leveraging AI to support clinical decision-making and offering a new approach to early dementia detection. © 2025, CC BY.

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Approximate diffusion tractography from FLAIR MRI and anatomical context using recurrent neural networks

Approximate diffusion tractography from FLAIR MRI and anatom...

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Medical Imaging 2025: Image Processing

作者： Li, Zhiyuan Kim, Michael E. Yu, Tian Kanakaraj, Praitayini Yao, Tianyuan Gao, Chenyu Resnick, Susan M. Beason-Held, Lori L. Habes, Mohamad Cai, Leon Y. Landman, Bennett A. Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Dept. of Computer Science Vanderbilt University NashvilleTN United States Laboratory of Behavioral Neuroscience National Institute on Aging BaltimoreMD United States Neuroimage Analytics Laboratory Glenn Biggs Institute Neuroimaging Core Glenn Biggs Institute for Neurodegenerative Diseases University of Texas Health San Antonio San AntonioTX United States Dept. of Internal Medicine Johns Hopkins Bayview Medical Center BaltimoreMD United States Dept. of Radiology & Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States

ISBN: (纸本)9781510685901

Diffusion MRI (dMRI) tractography methods provide a valuable method for in-vivo estimation of whole-brain white matter pathways that are commonly assumed to rely on microstructure models derived from dMRI. However, recent pioneering works have demonstrated that the accuracy of white-matter measurements computed from T1-weighted (T1w) MRI tractography is on a similar level to scan-rescan variability in dMRI tractography. This revelation raises new questions about understanding tractography: Is it primarily a dMRI microstructural phenomenon, and how different can it be when estimated from other imaging modalities? In this study, we propose a framework to approximate tractography from fluid-attenuated inversion recovery (FLAIR) MRI and examine its performance compared to tractography based on diffusion and T1w MRI. We adapt the teacher-student recurrent neural network (RNN) model from existing work on T1w tractography. Additionally, we use brain segmentation maps as the anatomical context. We conduct white matter bundle analysis and compare various metrics with those from T1w tractography and the traditional dMRI tractography. FLAIR tractography achieved significant different performance compared to T1 tractography evaluated by Dice similarity coefficient (p=0.004) and bundle adjacency streamlines distance (p=0.012). An average absolute difference of 23% was observed in eight bundle shape measurements between FLAIR tractography and traditional dMRI tractography. Both qualitative and quantitative results suggest that tractography based on FLAIR MRI is feasible and underscore the need for comprehensive research to understand tractography in the broader context of multi-modality brain MRI. © 2025 SPIE.

关键词： Diffusion tensor imaging

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MERNet: Multimodal Emotion Recognition in Conversation Network via Cross-Modal Knowledge Distillation and Contrastive Learning

MERNet: Multimodal Emotion Recognition in Conversation Netwo...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sooyeon Hwang Gahgene Gweon Eunseong Kwon Interdisciplinary Program in Artificial Intelligence Seoul National University South Korea Graduate School of Convergence Science and Technology Seoul National University South Korea Dept. of Computer Science and Engineering POSTECH South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.

关键词： Knowledge engineering Bridges Emotion recognition Visualization Computational modeling Contrastive learning Benchmark testing Linguistics Big Data

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Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data

arXiv

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arXiv 2025年

作者： Usman, Mohammad Varea, Olga Radeva, Petia I. Canals, Josep M. Abante, Jordi Ortiz, Daniel Dept. of Mathematics & Computer Science Universitat de Barcelona Barcelona Spain Dept. of Biomedical Sciences Faculty of Medicine and Health Sciences Institute of Neuroscience University of Barcelona Barcelona Spain Creatio Production and Validation Center of advanced therapies Universitat de Barcelona Barcelona Spain Barcelona Spain

Neurodegenerative diseases (NDDs) are complex and lack effective treatment due to their poorly understood mechanism. The increasingly used data analysis from Single nucleus RNA Sequencing (snRNA-seq) allows to explore transcriptomic events at a single cell level, yet face challenges in interpreting the mechanisms underlying a disease. On the other hand, Neural Network (NN) models can handle complex data to offer insights but can be seen as black boxes with poor interpretability. In this context, explainable AI (XAI) emerges as a solution that could help to understand disease-associated mechanisms when combined with efficient N models. However, limited research explores XAI in single-cell data. In this work, we implement a method for identifying disease-related genes and the mechanistic explanation of disease progression based on model combined with SHAP. We analyze available Huntington's disease (HD) data to identify both HD-altered genes and mechanisms by adding Gene Set Enrichment Analysis (GSEA) comparing two methods, differential gene expression analysis (DGE) and NN combined with SHAP approach. Our results show that DGE and SHAP approaches offer both common and differential sets of altered genes and pathways, reinforcing the usefulness of XAI methods for a broader perspective of disease. © 2025, CC BY-NC-ND.

关键词： Gene expression

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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