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Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

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Multimedia Tools and Applications 2025年第10期84卷 7493-7518页

作者： Raju, Chintha Sri Pothu Neelapu, Bala Chakravarthy Laskar, Rabul Hussain Muhammad, Ghulam Speech and Image Processing Lab Department of ECE National Institute of Technology Silchar Assam Silchar788010 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Stroke poses a significant risk to human life. Segmenting and immediately treating the stroke core stops its further development, therefore, enhancing the likelihood of survival. Convolutional neural networks (CNN) have been very successful in medical image segmentation, namely in the field of deep learning, and have produced the most advanced outcomes. Multi-modal images provide superior outcomes in the segmentation of stroke lesions compared to single-modal images. The integration of input from several modalities at various levels is crucial in determining performance and producing diverse outcomes in deep learning models that use multimodalities. Further investigation is required to explore the optimal methods for processing multimodal data in CNNs, the influence of fusion on CNN learning, and the effect of fusion strategies on lesions of varying sizes. To examine the impact of a multi-modal fusion method on lesion segmentation, we assessed four models using distinct fusion techniques, including early, late, bottleneck, and hierarchical fusions. This study discusses the various fusion procedures used in segmenting the lesion using computed tomography perfusion data. In addition, both quantitative and qualitative assessments, including deep feature analysis and feature similarity, were conducted to assess the impact of the fusion technique on the model’s performance. Furthermore, we examined the influence of fusion techniques on the size of the lesion. In addition, we analyzed the advantages and disadvantages of several multi-modal fusion systems. Our findings demonstrate that the bottleneck fusion technique got the highest dice score, 0.582, on the Ischemic Stroke Lesion Segmentation 2018 validation data as a result of its capacity to construct complex relationships across several modalities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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A One-shot/Few-shot Interactive Segmentation Method for CT image Segmentation

A One-shot/Few-shot Interactive Segmentation Method for CT I...

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Medical Imaging 2025: Computer-Aided Diagnosis

作者： Dai, Jian Udupa, Jayaram K. Torigian, Drew A. Tong, Yubing Liu, Tiange School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Medical image Processing Group Department of Radiology University of Pennsylvania PhiladelphiaPA19104 United States School of Intelligence Science and Technology University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781510685925

Medical image ground truth annotation is important in clinical workflows for diagnosis, disease staging, prognostication of outcome, treatment planning, and treatment response assessment. However, annotating medical images requires significant human effort, especially for three-dimensional (3D) medical images. Deep learning techniques have significantly contributed to the methods of medical image annotation. Nonetheless, highly accurate automatic segmentation methods still require large-scale labeled data for training, which hinders the application in practice, especially for objects that are newly defined and/or are not well defined in the image and/or are spatially sparse with poor boundaries. Deep-learning(DL)-based interactive segmentation methods offer a trade-off between training cost and prediction performance. By leveraging extra regional prompts, these methods gain an unparalleled ability to locate the target object compared to automatic methods. This region recognition ability is essential for improving DL segmentation performance when training data are limited. In this paper, we present an interactive DL segmentation method for 3D computed tomography (CT) images, especially for the situation of one-shot/few-shot learning. Specifically, we propose a novel click prompt simulation method for training 3D interactive segmentation models inspired by the annotating procedures and focuses of human experts. The proposed prompt simulation consists of the object boundary prompt to indicate the global and local characteristics of the object and the mis-predicted region prompt to refine existing segmentations. We conduct experiments involving multiple anatomical structure segmentation tasks on our publicly available abdomen dataset under the condition of one-shot/few-shot learning. The experimental results demonstrate that the interactive segmentation method is more efficacious than its fully automated counterpart, which highlights the significant potential of applyin

关键词： image segmentation

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Tissue P Systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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Attention-based Fusion for Stroke Lesion Segmentation on Computed Tomography Perfusion Data

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷

作者： Raju, Chintha Sri Pothu Laskar, Rabul Hussain Ali, Zulfiqar Muhammad, Ghulam Speech and Image Processing Lab Department of ECE National Institute of Technology Silchar Assam Silchar India University of Essex Colchester United Kingdom Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Riyadh Saudi Arabia

In recent times, stroke has emerged as a significant threat to humans, transforming affected brain tissue into core and penumbra regions. As the penumbra becomes irreversible over time, early core region segmentation is crucial. Automatic segmentation systems offer an efficient alternative to manual segmentation and aid radiologists in stroke lesion segmentation using computed tomography and Computed Tomography Perfusion (CTP) maps that comprise four parameter maps. This automatic segmentation is increasingly used in interactive, multimedia-based systems for diagnostic tools and AI-driven health applications. Top-performing models that follow the patch-processing approach suffer from high inference times. To incorporate effective feature extraction at image-level inferences, which reduces the inference time, we present a hybrid fusion technique that combines early and bottleneck fusion, leveraging two separate encoders for effective feature extraction. Moreover, fusing the information from various fusion methods arbitrarily may not yield optimal results. Consequently, we have introduced two attention modules, i.e., cross-modal attention and cross-fusion attention modules, designed for the effective integration of features derived from diverse modalities and multiple fusion strategies, respectively. The findings highlight a considerable reduction in computational time alongside achieving a comparable Dice score. Additionally, the incorporation of hybrid fusion and attention modules in the baseline notably increased the Dice score from 0.482 to 0.521 in the validation dataset and achieved 0.48 in the test dataset of ISLES 2018. It also demonstrates competitive performance compared to existing models while maintaining efficient prediction times. © 2025 Copyright held by the owner/author(s).

关键词： image segmentation

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Aortic KD former: aortic multiclass segmentation using SegFormer via knowledge distillation

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Machine Learning: Science and Technology 2025年第2期6卷

作者： Mohamed Soliman, Nancy Awadalla, Medhat Elhelw, Mohamed Elattar, Mustafa Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza Egypt Department of Computers and Systems Helwan University Helwan Cairo Egypt School of Information Technology and Computer Science Nile University Giza Egypt

In this paper, we investigate the effectiveness of knowledge distillation for semantic segmentation of aortic structures using deep learning models. We employ SegFormer B5 as the teacher model and SegFormer B0 as the student model. Knowledge distillation is applied through three methods: output-based distillation, inner feature maps-based distillation, and integrated distillation, which combines both logits and feature maps. Our results show that while the student model (SegFormer B0) has significantly fewer parameters (3,715,686) compared to the teacher model (SegFormer B5) with 84,597,958 parameters, it achieves performance closely approaching that of the teacher. Among the evaluated techniques, inner feature map distillation yields the best performance, with a mean IoU of 0.9994, a mean Dice score of 0.9549, and a loss of 0.0020, demonstrating its superior capability in transferring knowledge effectively. The study highlights the potential of inner feature map distillation as the most effective method for achieving high performance with reduced model complexity. © 2025 The Author(s). Published by IOP Publishing Ltd.

关键词： Semantic Segmentation

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Stability Enhancement of Inverter-Current Feedback Grid-Following Inverters Using a Passivity-Based Voltage Controller

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IEEE Transactions on Energy Conversion 2025年

作者： Huang, Yukai Xu, Jinbang Ye, Jie Huang, Songtao Ren, Ziao Shen, Anwen Hu, Yihua Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China King's College London Department of Engineering LondonWC2R 2LS United Kingdom

Grid-following inverters remain the primary interface for modern renewable energy systems due to their strong power output performance. As the global demand for clean and efficient energy conversion increases, optimizing the stability of inverter systems is crucial for integrating renewable sources into the power grid. In certain energy conversion scenarios, current sensors are placed on the inverter side due to equipment constraints and space limitations, which can impact the stability of the inverter. Although the stability of inverter-side current feedback (ICF) configurations has been studied, methods to further enhance their stability remain underexplored. In this paper, the stability of ICF grid-following inverters is analyzed using a passivity-based design approach, focusing on a point of common coupling voltage controller. The proposed controller is thoroughly analyzed, and a detailed design strategy is presented to ensure stability under varying grid impedance conditions. Experimental results validate the theoretical analysis, demonstrating the effectiveness of the proposed design in enhancing the stability of ICF grid-following inverters. These advancements contribute to improving energy conversion efficiency and reliability. © 2025 IEEE.

关键词： Energy conversion efficiency

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Multi-Scale Local and Global Feature Fusion for Blind Quality Assessment of Enhanced images

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cao, Jingchao Zhang, Shuai Liu, Yutao Gao, Feng Gu, Ke Zhai, Guangtao Dong, Junyu Kwong, Sam Ocean University of China School of Computer Science and Technology Qingdao266100 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China Lingnan University Department of Computing and Decision Science Hong Kong

image enhancement plays a crucial role in computer vision by improving visual quality while minimizing distortion. Traditional methods enhance images through pixel value transformations, yet they often introduce new distortions. Recent advancements in deep learning-based techniques promise better results but challenge the preservation of image fidelity. Therefore, it is essential to evaluate the visual quality of enhanced images. However, existing quality assessment methods frequently encounter difficulties due to the unique distortions introduced by these enhancements, thereby restricting their effectiveness. To address these challenges, this paper proposes a novel blind image quality assessment (BIQA) method for enhanced natural images, termed multi-scale local feature fusion and global feature representation-based quality assessment (MLGQA). This model integrates three key components: a multi-scale Feature Attention Mechanism (FAM) for local feature extraction, a Local Feature Fusion (LFF) module for cross-scale feature synthesis, and a Global Feature Representation (GFR) module using Vision Transformers to capture global perceptual attributes. This synergistic framework effectively captures both fine-grained local distortions and broader global features that collectively define the visual quality of enhanced images. Furthermore, in the absence of a dedicated benchmark for enhanced natural images, we design the Natural image Enhancement Database (NIED), a largescale dataset consisting of 8,581 original images and 102,972 enhanced natural images generated through a wide array of traditional and deep learning-based enhancement techniques. Extensive experiments on NIED demonstrate that the proposed MLGQA model significantly outperforms current state-of-the-art BIQA methods in terms of both prediction accuracy and robustness. © 1991-2012 IEEE.

关键词： image quality

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Measurements with Angular Sensitive Electric Field Mill in Thunderstorms

Measurements with Angular Sensitive Electric Field Mill in T...

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Atmospheric Monitoring for High Energy Astroparticle Detectors 2024, AtmoHEAD 2024

作者： Kákona, Martin Kákona, Jakub Dvořák, Roman Ambrožová, Iva Velychko, Olena Lužová, Martina Šlegl, Jakub Sommer, Marek Hanousek, Vít Langer, Ronald Štefánik, Samuel Ploc, Ondřej Department of Radiation Dosimetry Nuclear Physics Institute Czech Academy of Sciences Řež Czech Republic Department of Space Physics and Detached Observatory at Lomnicky Stit Institute of Experimental Physics SAS Košice Slovakia Universal Scientific Technologies s.r.o. Soběslav Czech Republic Faculty of Nuclear Sciences and Physical Engineering Czech Technical University Prague Czech Republic Department of Image Processing Institute of Information Theory and Automation Czech Academy of Sciences Prague Czech Republic

Electric Field Mills (EFMs) are critical tools in atmospheric sciences for measuring electric fields within thunderstorms. Traditional EFMs primarily record field magnitudes and often lack the precise temporal resolution and directionality necessary for in-depth storm analysis. Our research introduces THUNDERMILL01, an advanced EFM that determines both the magnitude and direction of electric fields, significantly enhancing temporal resolution. This study details the deployment of THUNDERMILL01 alongside a standard EFM at the Lomnický štít high-altitude observatory in Slovakia for comparative analysis. Initial findings indicate an enhanced capability to map charge distribution dynamics within thunderclouds, promising improvements in lightning prediction and thunderstorm understanding. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Electric field measurement

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Dataset and Metric for Quality Assessment of HDR Tone Mapping: Detail Visibility, Color Naturalness, and Overall Quality

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IEEE Transactions on Multimedia 2025年

作者： Jiang, Qiuping Li, Xiwen Wang, Xinyi Wang, Zhihua Zhai, Guangtao Ningbo University School of Information Science and Engineering Ningbo315211 China Hangzhou DtDream Science and Technology Ltd. Hangzhou310024 China City University of Hong Kong Department of Computer Science Kowloon999077 Hong Kong Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China

Tone-Mapping Operators (TMOs) aim at converting high dynamic range (HDR) images into standard dynamic range (SDR) ones that are suitable for being displayed on standard screens. As the visual quality of tone-mapped image (TMI) is paramount, conducting quality assessment of TMIs becomes crucial. Despite the growing body of research on TMI quality assessment, the existing metrics are often limited to a narrow selection of hand-picked examples generated by a restricted range of TMOs. Consequently, their ability of generalizing to the wide array of TMIs encountered in practical scenarios remains unclear. Moreover, the quality degradation in practical TMIs can be intricate, diverse, and complex. To overcome these limitations, we construct so far the largest subjective-annotated TMI quality assessment dataset which comprises a total number of 14,000 TMIs generated by applying 20 representative TMOs to 700 HDR images. The dataset is accompanied by subjective scores that encompass multiple quality dimensions, i.e., TMI quality dataset in terms of Detail visibility, Color naturalness, and overall Quality (TDCQ). In addition, we also design a multi-branch deep neural network tailored to characterize the multi-dimensional quality perception of TMIs, i.e., Color naturalness-, Detail visibility-aware TMI Quality (CDTIQ) metric, allowing for a comprehensive and multifaceted quality assessment of TMIs. Through extensive experiments, we demonstrate the superiority of our proposed metric, showcasing a higher correlation with subjective rating results compared to other relevant no-reference image quality metrics. Our newly constructed TDCQ database and the implementation of our CDTIQ model are available at https://***/zzeu001/CDTIQ. © 1999-2012 IEEE.

关键词： Mapping

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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