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Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images

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arXiv 2025年

作者： Sang, Shengtian Jahanandish, Hassan Li, Cynthia Xinran Bhattachary, Indrani Lee, Jeong Hoon Zhang, Lichun Vesal, Sulaiman Ghanouni, Pejman Fan, Richard Sonn, Geoffrey A. Rusu, Mirabela Stanford University Department of Radiology 300 Pasteur Drive StanfordCA94305 United States University of Miami Department of Computer Science Coral GablesFL33124 United States Stanford University Department of Urology 300 Pasteur Drive StanfordCA94305 United States Stanford University Institute of Computational and Mathematical Engineering 475 Via Ortega StanfordCA94305 United States Dartmouth College Department of Biomedical Data Science 1 Medical Center Drive LebanonNH03756 United States

Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by combining MRI’s detailed visualization with TRUS’s real-time guidance, it is a complex and time-intensive procedure that relies heavily on manual annotations, leading to potential errors. To address these challenges, we propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations. Unlike traditional multimodal fusion approaches that rely on naive data concatenation, our method integrates a registration-segmentation framework to align and leverage spatial information between MRI and TRUS modalities. This alignment enhances segmentation accuracy and reduces reliance on manual effort. Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132) methods, with significant improvements (p Copyright © 2025, The Authors. All rights reserved.

关键词： Urology

Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-center Study

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arXiv 2025年

作者： Jahanandish, Hassan Sang, Shengtian Li, Cynthia Xinran Vesal, Sulaiman Bhattacharya, Indrani Lee, Jeong Hoon Fan, Richard Sonn, Geoffrey A. Rusu, Mirabela Stanford University Department of Urology 300 Pasteur Drive StanfordCA94305 United States Stanford University Department of Radiology 300 Pasteur Drive StanfordCA94305 United States University of Miami Department of Computer Science Coral GablesFL33124 United States Stanford University Institute of Computational and Mathematical Engineering 475 Via Ortega StanfordCA94305 United States Dartmouth College Department of Biomedical Data Science 1 Medical Center Drive LebanonNH03756 United States

Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-based detection of clinically significant prostate cancer (CsPCa). However, MRI-detected lesions must still be mapped to transrectal ultrasound (TRUS) images during biopsy, which results in missing CsPCa. This study systematically evaluates a multimodal AI framework integrating MRI and TRUS image sequences to enhance CsPCa identification. The study included 3110 patients from three cohorts across two institutions who underwent prostate biopsy. The proposed framework, based on the 3D UNet architecture, was evaluated on 1700 test cases, comparing performance to unimodal AI models that use either MRI or TRUS alone. Additionally, the proposed model was compared to radiologists in a cohort of 110 patients. The multimodal AI approach achieved superior sensitivity (80%) and Lesion Dice (42%) compared to unimodal MRI (73%, 30%) and TRUS models (49%, 27%). Compared to radiologists, the multimodal model showed higher specificity (88% vs. 78%) and Lesion Dice (38% vs. 33%), with equivalent sensitivity (79%). Our findings demonstrate multimodal AI’s potential to improve CsPCa lesion targeting during biopsy and treatment planning, surpassing current unimodal models and radiologists;ultimately improving outcomes for prostate cancer patients. Copyright © 2025, The Authors. All rights reserved.

关键词： Biopsy

Long-term mortality in ICU patients with hematological malignancies: Impact of organ support duration and ICU length of stay

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Journal of critical care 2025年 89卷 155122页

作者： Martijn Otten Bob J H van Kempen Brittney van der Woude Tariq A Dam Rolf K Gigengack Marcella C A Müller Armand R J Girbes Bart J Biemond Harm-Jan de Grooth Paul W G Elbers Department of Intensive Care Medicine Center for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam Public Health Amsterdam institute for Immunology and Infectious Diseases Amsterdam UMC Vrije Universiteit University of Amsterdam Amsterdam the Netherlands Quantitative Data Analytics Group Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam the Netherlands. Electronic address: m.otten1@amsterdamumc.nl. Department of Intensive Care Medicine Center for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam Public Health Amsterdam institute for Immunology and Infectious Diseases Amsterdam UMC Vrije Universiteit University of Amsterdam Amsterdam the Netherlands. Department of Intensive Care Medicine UMC Utrecht Utrecht the Netherlands. Quantitative Data Analytics Group Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam the Netherlands. Department of Hematology Amsterdam UMC the Netherlands.

BACKGROUND:Patients with hematological malignancies face unique challenges due to immunosuppression and treatment-related complications. This study aims to evaluate long-term survival probabilities for ICU patients with hematological malignancy as a function of length of stay in the ICU and organ support duration, compared to non-hematological ICU patients. METHODS:In this retrospective multicenter cohort study, patients with hematological malignancies were matched with four controls based on APACHE IV scores and year of admission. Mortality at 1 year following ICU admission was assessed using landmark analyses, stratified by length-of-stay and organ support duration. RESULTS:A total of 2.775 admissions were included (555 hematological malignancy and 2.220 matched controls). One-year mortality for patients with hematological malignancy (matched controls) with ICU length of stay of at least 1, 7 and 14 days was 64 % (43 %), 72 % (42 %), and 74 % (34 %). For those receiving 1, 7 and 14 days of renal replacement therapy 1-year mortality was 79 % (60 %), 79 % (52 %), and 85 % (55 %). For those receiving blood transfusion for 1, 7 and 14 days 1-year mortality was 74 % (47 %), 81 % (51 %), and 86 % (50 %). For those receiving triple organ support for 1,7 and 14 days 1-year mortality was 84 % (54 %), 84 % (51 %), and 94 % (56 %). CONCLUSIONS:One-year mortality among patients with hematological malignancy compared to matched controls was higher at the first day of admission and stayed higher for patients who remained in the ICU. The observed one-year mortality seemed to increase with initiation of prolonged renal replacement therapy, blood transfusion and combination of at least 3 organs supported, but not for the other forms of organ support.

关键词： Hematology intensive care medicine Length of stay Mortality Organ support Survival

Unsupervised Behaviour Analysis of News Consumption in Turkish Media

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arXiv 2022年

作者： Makaroğlu, Didem Cakir, Altan Töreyin, Behçet Uğur Demirören Teknoloji A.S. İstanbul Turkey Signal Processing for Computational Intelligence Group Informatics Institute Istanbul Technical University Istanbul Turkey Physics Engineering Faculty of Science and Letters Istanbul Technical University Istanbul Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul Turkey

Clickstream data, which come with a massive volume generated by human activities on websites, have become a prominent feature for identifying readers’ characteristics by newsrooms after the digitization of news outlets. It is essential to have elastic architectures to process the streaming data, particularly for unprecedented traffic, enabling conducting more comprehensive analyses, such as recommending mostly related articles to readers. Although the nature of clickstream data has a similar logic within websites, it has inherent limitations to recognize human behaviours when looking from a broad perspective, which brings the need to limit the problem in niche areas. This study investigates the anonymized readers’ click activities on the organizations’ websites to identify news consumption patterns following referrals from Twitter, who incidentally reach but propensity is mainly routed news content. The investigation is widened to a broad perspective by linking the log data with news content to enrich the insights rather than sticking into the web journey. Methodologies for ensemble cluster analysis with mixed-type embedding strategies are applied and compared to find similar reader groups and interests independent of time. Various internal validation perspectives are used to determine the optimality of the quality of clusters, where the Calinski Harabasz Index (CHI) is found to give a generalizable result. Our findings demonstrate that clustering a mixed-type dataset approaches the optimal internal validation scores, which we define to discriminate the clusters and algorithms considering applied strategies, when embedded by Uniform Manifold Approximation and Projection (UMAP) and using a consensus function as a key to access the most applicable hyper parameter configurations in the given ensemble rather than using consensus function results directly. Evaluation of the resulting clusters highlights specific clusters repeatedly present in the separated monthly sample

关键词： Digital storage

Unbiasing procedures for scale-invariant multi-reference alignment

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arXiv 2021年

作者： Hirn, Matthew Little, Anna Department of Computational Mathematics Science and Engineering Department of Mathematics Center for Quantum Computing Science and Engineering Michigan State University East LansingMI48824 United States Department of Mathematics Utah Center For Data Science University of Utah Salt Lake CityUT84112 United States

This article discusses a generalization of the 1-dimensional multi-reference alignment problem. The goal is to recover a hidden signal from many noisy observations, where each noisy observation includes a random translation and random dilation of the hidden signal, as well as high additive noise. We propose a method that recovers the power spectrum of the hidden signal by applying a data-driven, nonlinear unbiasing procedure, and thus the hidden signal is obtained up to an unknown phase. An unbiased estimator of the power spectrum is defined, whose error depends on the sample size and noise levels, and we precisely quantify the convergence rate of the proposed estimator. The unbiasing procedure relies on knowledge of the dilation distribution, and we implement an optimization procedure to learn the dilation variance when this parameter is unknown. Our theoretical work is supported by extensive numerical experiments on a wide range of signals. Copyright © 2021, The Authors. All rights reserved.

关键词： Power spectrum

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal–organic frameworks for carbon capture

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arXiv 2023年

作者： Park, Hyun Yan, Xiaoli Zhu, Ruijie Huerta, Eliu A. Chaudhuri, Santanu Cooper, Donny Foster, Ian Tajkhorshid, Emad Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Visualization Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Metal–organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m molg−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

Estimation of the Second Moment Based on Rounded data

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Journal of Mathematical sciences (United States) 2019年第6期237卷 819-825页

作者： Samsonov, S.V. Ushakov, N.G. Ushakov, V.G. Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russian Federation Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway Moscow State University Moscow Russian Federation

Sample moments are unbiased estimators of theoretical moments (if the latter exist). In practice, however, observations are rounded under registration, which leads to systematic errors. In [1–3] it was shown that random measurement errors can provide the reduction of rounding errors, when the expectation is estimated by the first sample moment. This gives a possibility to manage the rounding error of the result, if one can add some noise to observations before registration. Moreover, this error can be made arbitrarily small. Now we find conditions under which this takes place for the second moment. © 2019, Springer science+Business Media, LLC, part of Springer Nature.

关键词： Estimators Registration second moment Noise Systematic errors Error measure Moment of force Rounding error Tea random measurement

Investigating the relation between environment and internal structure of massive elliptical galaxies using strong lensing

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arXiv 2024年

作者： Adnan, S.M. Rafee Hasan, Muhammad Jobair Al-Imtiaz, Ahmad Robin, Sulyman H. Shwadhin, Fahim R. Shajib, Anowar J. Nahid, Mamun Hossain Tanver, Mehedi Hasan Akter, Tanjela Jahan, Nusrath Jafar, Zareef Rashid, Mamunur Biswas, Anik Chowdhury, Akbar Ahmed Feardous, Jannatul Rahaman, Ajmi Ridwan, Masuk Sharma, Rahul D. Chowdhury, Zannat Hossain, Mir Sazzat Department of Physics and Astronomy University of Louisville LouisvilleKY40208 United States Department of Physics Shahjalal University of Science and Technology Sylhet3114 Bangladesh Copernicus Astronomical Memorial of SUST Shahjalal University of Science and Technology Sylhet3114 Bangladesh Department of Electrical and Electronic Engineering Bangladesh Army University of Engineering & Technology Qadirabad Natore6431 Bangladesh Department of Astronomy & Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Mathematics Shahjalal University of Science and Technology Sylhet3114 Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka1229 Bangladesh Department of Computer Science and Engineering Brac University Dhaka1212 Bangladesh Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka1205 Bangladesh Department of Physical Science Independent University Bangladesh Dhaka1229 Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka1229 Bangladesh

Strong lensing directly probes the internal structure of the lensing galaxies. In this paper, we investigate the relation between the internal structure of massive elliptical galaxies and their environment using a sample of 15 strong lensing systems. We performed lens modeling for them using lenstronomy and constrained the mass and light distributions of the deflector galaxies. We adopt the local galaxy density as a metric for the environment and test our results against several alternative definitions of it. We robustly find that the centroid offset between the mass and light is not correlated with the local galaxy density. This result supports using centroid offsets as a probe of dark matter theories since the environment's impact on it can be treated as negligible. Although we find a strong correlation between the position angle offset and the standard definition of the local galaxy density, consistent with previous studies, the correlation becomes weaker for alternative definitions of the local galaxy density. This result weakens the support for interpreting the position angle misalignment as having originated from interaction with the environment. Furthermore, we find the 'residual shear' magnitude in the lens model to be uncorrelated with the local galaxy density, supporting the interpretation of the residual shear originating, in part, from the inadequacy in modeling the angular structure of the lensing galaxy and not solely from the structures present in the environment or along the line of sight. © 2024, CC BY.

关键词： Probes

Intense harmonics with time-varying orbital angular momentum from relativistic plasma mirrors

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arXiv 2021年

作者： Wang, Jingwei Zepf, Matt Leng, Yuxin Li, Ruxin Rykovanov, Sergey G. Shanghai 201800 China Collaborative Innovation Center of IFSA Shanghai Jiao Tong University Shanghai 200240 China Helmholtz Institute Jena Fröbelstieg 3 Jena07743 Germany Faculty of Physics and Astronomy Friedrich-Schiller-Universität Jena Jena07743 Germany High Performance Computing and Big Data Laboratory Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow121205 Russia

In this Letter using three-dimensional particle-in-cell simulations and analytical considerations we demonstrate intense high-order plasma surface harmonics carrying a time-varying orbital angular momentum (OAM) - the self-torque. We show that by using two laser beams with different OAMs l1 and l2 and a certain delay between each other and shooting them obliquely on an overdense plasma target, one can generate harmonics with OAM spanning nl1 to nl2, where n is the order of the harmonic. Such intense self-torqued harmonics can offer new possibilities in ultrafast spectroscopy. © 2021, CC BY.

关键词： Harmonic analysis