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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

Procedia computer Science 2024年 235卷 1824-1831页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation

arXiv

引用

arXiv 2024年

作者： Tan, Hong Ye Cai, Ziruo Pereyra, Marcelo Mukherjee, Subhadip Tang, Junqi Schönlieb, Carola-Bibiane Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom School of Mathematical Sciences Shanghai Jiao Tong University China School of Mathematical & Computer Sciences Heriot-Watt University Edinburgh United Kingdom Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India School of Mathematics University of Birmingham United Kingdom

Imaging is a canonical inverse problem, where the task of reconstructing a ground truth from a noisy measurement is typically ill-conditioned or ill-posed. Recent state-of-the-art approaches for imaging use deep learning, spearheaded by unrolled and end-to-end models and trained on various image datasets. However, such methods typically require the availability of ground truth data, which may be unavailable or expensive, leading to a fundamental barrier that can not be addressed by choice of architecture. Unsupervised learning presents a powerful alternative paradigm that bypasses this requirement by allowing to learn directly from noisy measurement data without the need for any ground truth. A principled statistical approach to unsupervised learning is to maximize the marginal likelihood of the model parameters with respect to the given noisy measurements. This paper proposes an unsupervised learning approach that leverages maximum marginal likelihood estimation and stochastic approximation computation in order to train a convex neural network-based image regularization term directly on noisy measurements, improving upon previous work in both model expressiveness and dataset size. Experiments demonstrate that the proposed method produces image priors that are comparable in performance to the analogous supervised models for various image corruption operators, maintaining significantly better generalization properties when compared to end-to-end methods. Moreover, we provide a detailed theoretical analysis of the convergence properties of our proposed *** Codes 62C12, 62F15, 65C40, 65J22 Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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High-dimensional orbital angular momentum multiplexing nonlinear holography

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Advanced Photonics 2021年第1期3卷 40-46页

作者： Xinyuan Fang Haocheng Yang Wenzhe Yao Tianxin Wang Yong Zhang Min Gu Min Xiaoa Nanjing University College of Engineering and Applied SciencesSchool of PhysicsNational Laboratory of Solid State MicrostructuresNanjingChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringCentre for Artificial-Intelligence NanophotonicsShanghaiChina University of Arkansas Department of PhysicsFayettevilleArkansasUnited States

Nonlinear holography has been identified as a vital platform for optical multiplexing holography because of the appearance of new optical ***,due to nonlinear wave coupling in nonlinear optical processes,the nonlinear harmonic field is coupled with the input field,laying a fundamental barrier to independent control of the interacting fields for *** propose and experimentally demonstrate high-dimensional orbital angular momentum(OAM)multiplexing nonlinear holography to overcome this *** dividing the wavefront of the fundamental wave into different orthogonal OAM channels,multiple OAM and polarization-dependent holographic images in both the fundamental wave and second-harmonic wave have been reconstructed independently in the spatial frequency domain through a type-II second harmonic generation ***,this method can be easily extended to cascadedχ2 nonlinear optical processes for multiplexing in more wavelength channels,leading to potential applications in multicasting in optical communications,multiwavelength display,multidimensional optical storage,anticounterfeiting,and optical encryption.

关键词： orbital angular momentum nonlinear holography second harmonic generation multiplexing holography

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Time-domain identification of distinct mechanisms for competing charge density waves in a rare-earth tritelluride

arXiv

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arXiv 2025年

作者： Su, Yifan Lv, B.Q. Zong, Alfred Müller, Aaron Chattopadhyay, Sambuddha Dolgirev, Pavel E. Singh, Anisha G. Straquadine, Joshua A.W. Choi, Dongsung Azoury, Doron Mogi, Masataka Fisher, Ian R. Demler, Eugene Gedik, Nuh Massachusetts Institute of Technology Department of Physics CambridgeMA02139 United States Tsung-Dao Lee Institute School of Physics and Astronomy Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai200240 China Departments of Physics and of Applied Physics Stanford University StanfordCA94305 United States SIMES SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Institute for Theoretical Physics ETH Zürich Zürich8093 Switzerland Lyman Laboratory Department of Physics Harvard University CambridgeMA02138 United States Geballe Laboratory for Advanced Materials Stanford University StanfordCA94305 United States Department of Applied Physics Stanford University StanfordCA94305 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States Department of Applied Physics University of Tokyo Bunkyo-ku Tokyo113-8656 Japan

Understanding the origin of phase transitions and the interactions between distinct phases remains a central task in condensed matter physics. Charge density wave (CDW) systems provide an ideal platform for investigating these phenomena. While the dominant CDW phases in many materials can be explained through Fermi surface nesting or electron-phonon interactions, certain CDW phase transitions remain poorly understood, challenging conventional paradigms. One notable example is rare-earth tritelluride ErTe3, which hosts two competing CDW orders. While the dominant CDW phase fits within the electron-phonon coupling framework, the formation mechanism of the subdominant CDW remains enigmatic. In this study, we combine time- and angle-resolved photoemission spectroscopy and time-dependent Ginzburg-Landau theory to establish a time-domain approach for probing phase transitions in solid-state systems. By analyzing the distinct recovery dynamics of the two CDW orders in ErTe3 following light excitation, we reveal a novel nucleation and growth mechanism that likely drives the secondary CDW phase transition. This work not only uncovers a previously unknown CDW formation mechanism in rare-earth tritellurides but also introduces a non-equilibrium framework for understanding phase transitions and phase competition in quantum materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Charge density waves

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Tunable Frequency Filter Based on Twisted Bilayer Photonic Crystal Slabs

Tunable Frequency Filter Based on Twisted Bilayer Photonic C...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Beicheng Lou Shanhui Fan Department of Applied Physics Stanford University Stanford CA USA Department of Electrical Engineering Ginzton Laboratory Stanford University Stanford CA USA

We introduce a tunable narrow-band-stop frequency filter based on twisted bilayer photonic crystal slabs in sub-wavelength regime. A desired lineshape is achieved by introducing additional uniform slabs. The filter ca... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Optical losses Diffraction Photonic crystals Lasers and electrooptics Slabs Electrooptical waveguides

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Named Entity Recognition in Italian Lung Cancer Clinical Reports using Transformers

Named Entity Recognition in Italian Lung Cancer Clinical Rep...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Paolo, Domenico Bria, Alessandro Greco, Carlo Russano, Marco Ramella, Sara Soda, Paolo Sicilia, Rosa Università Campus Bio-Medico di Roma Unit of Computer Systems & Bioinformatics Department of Engineering Italy University of Cassino and Southern Latium Department of Electrical and Information Engineering Cassino Italy Università Campus Bio-Medico di Roma Research Unit of Radiation Oncology Department of Medicine and Surgery Italy Fondazione Policlinico Universitario Campus Bio-Medico Operative Research Unit of Radiation Oncology Italy Fondazione Policlinico Universitario Campus Bio-Medico Operative Research Unit of Medical Oncology Italy Umeå University Department of Radiation Sciences Radiation Physics Biomedical Engineering Sweden

ISBN: (纸本)9798350337488

The widespread adoption of electronic health records (EHRs) offers a valuable opportunity to support clinical research by containing crucial patient information, including diagnoses, symptoms, medications, lab tests, and more. Despite the success of deep learning for biomedical Named Entity Recognition (NER), the literature in this field still presents a gap regarding applications focused on lung cancer for the Italian language. Hence, this paper presents a transformer-based approach to extract named entities from Italian clinical notes related to Non-Small Cell Lung Cancer (NSCLC). We introduce a novel set of 25 clinical entities related to NSCLC building a corpus annotated for NER. We apply a state-of the-art model pre-trained on Italian biomedical texts to the manually annotated clinical reports of a cohort of 257 patients suffering from NSCLC, successfully dealing with class-imbalance problems and obtaining promising performance (average F1-score of 84.3%). We also compared our method with two other pre-trained state-of-the-art models showing that the domain specific knowledge offered by the proposed approach is necessary to achieve higher performance. These findings also showcase the feasibility of using transformers to extract biomedical information in the Italian language. © 2023 IEEE.

关键词： deep learning EHRs NER NSCLC trasformer

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CShaperApp:Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

引用

Quantitative Biology 2024年第3期12卷 329-334页

作者： Jianfeng Cao Lihan Hu Guoye Guan Zelin Li Zhongying Zhao Chao Tang Hong Yan Department of Electrical Engineering City University of Hong KongHong KongChina Centre for Intelligent Multidimensional Data Analysis Limited Hong KongChina College of Computer and Information Hohai UniversityNanjingChina Center for Quantitative Biology Peking UniversityBeijingChina Department of Biology Hong Kong Baptist UniversityHong KongChina State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist UniversityHong KongChina Peking-Tsinghua Center for Life Sciences Peking UniversityBeijingChina School of Physics Peking UniversityBeijingChina

Caenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant *** this study,we developed a desktop software CShaperApp to segment fluorescence-labeled images of cell membranes and analyze cellular morphologies interactively during *** *** on the previously proposed framework CShaper,CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine-tuned one adapted to their in-house *** results show that it takes about 30 min to process a three-dimensional time-lapse(4D)dataset,which consists of 150 image stacks at a~1.5-min interval and covers *** embryogenesis from the 4-cell to 350-cell *** robustness of CShaperApp is also validated with the datasets from different ***,modularized implementation increases the flexibility in multi-task applications and promotes its flexibility for future *** cell morphology over development has emerged as a focus of interest in developmental biology,CShaperApp is anticipated to pave the way for those studies by accelerating the high-throughput generation of systems-level quantitative data *** software can be freely downloaded from the website of Github(cao13jf/CShaperApp)and is executable on Windows,macOS,and Linux operating systems.

关键词： C.elegans embryogenesis cellular morphology cellular segmentation deep learning desktop software

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Optimal self-concordant barriers for quantum relative entropies

arXiv

引用

arXiv 2022年

作者： Fawzi, Hamza Saunderson, James Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Electrical and Computer Systems Engineering Monash University VIC3800 Australia

Quantum relative entropies are jointly convex functions of two positive definite matrices that generalize the Kullback-Leibler divergence and arise naturally in quantum information theory. In this paper, we prove self-concordance of natural barrier functions for the epigraphs of various quantum relative entropies and divergences. Furthermore we show that these barriers have optimal barrier parameter. These barriers allow convex optimization problems involving quantum relative entropies to be directly solved using interior point methods for non-symmetric cones, avoiding the approximations and lifting techniques used in previous approaches. More generally, we establish the self-concordance of natural barriers for various closed convex cones related to the noncommutative perspectives of operator concave functions, and show that the resulting barrier parameters are optimal. Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

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Statistics dependent spectral properties of random arrays of particles

arXiv

引用

arXiv 2023年

作者： Audhkhasi, Romil Zhelyeznyakov, Maksym Brunton, Steven Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Physics University of Washington SeattleWA98195 United States

The ability to tailor the spectral response of photonic devices is paramount to the advancement of a broad range of applications. The vast design space offered by disordered optical media provide enhanced functionality for spectral tailoring, although it is challenging to map the spectral properties of such complex systems to their structural attributes. In this work, we investigate correlations between the statistics underlying the structure of random arrays of particles and their spectral properties. We consider 1D and 2D arrays of dielectric nanorods suspended in vacuum and numerically study their optical scattering properties in the 300-800 nm wavelength range. We show that the scattering cross section of a random particle array is primarily governed by its configuration statistics and is independent of its exact instantiation or the number of its constituent particles. We further exploit the strong correlations between the statistics and spectral properties of random particle arrays to predict their spectral response. By using a semi-analytical nearest neighbor coupling model, we produce accurate qualitative estimates of the spectral responses of both one and two-dimensional random arrays for different configuration statistics. The results presented in this manuscript will open new avenues for optimizing large-scale random systems to achieve enhanced optical functionalities for a wide variety of applications. © 2023, CC BY.

关键词： Statistics

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HyperImpute: Generalized Iterative Imputation with Automatic Model Selection

arXiv

引用

arXiv 2022年

作者： Jarrett, Daniel Cebere, Bogdan Liu, Tennison Curth, Alicia Schaar, Mihaela van der Department of Applied Mathematics & Theoretical Physics University of Cambridge United Kingdom Department of Electrical Engineering University of California Los Angeles United States

Consider the problem of imputing missing values in a dataset. On the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability of learning conditional distributions directly, but suffer from the practical requirement for appropriate model specification of each and every variable. On the other hand, recent methods using deep generative modeling benefit from the capacity and efficiency of learning with neural network function approximators, but are often difficult to optimize and rely on stronger data assumptions. In this work, we study an approach that marries the advantages of both: We propose HyperImpute, a generalized iterative imputation framework for adaptively and automatically configuring column-wise models and their hyperparameters. Practically, we provide a concrete implementation with out-of-the-box learners, optimizers, simulators, and extensible interfaces. Empirically, we investigate this framework via comprehensive experiments and sensitivities on a variety of public datasets, and demonstrate its ability to generate accurate imputations relative to a strong suite of benchmarks. Contrary to recent work, we believe our findings constitute a strong defense of the iterative imputation paradigm. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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