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Cross-modal Domain Adaptive Instance Segmentation in SAR images via Instance-aware Adaptation 18th

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Cross-modal Domain Adaptive Instance Segmentation in SAR Ima...

18th Chinese Conference on image and Graphics technology and Application Conference, IGTA 2023

作者： Cheng, Xiao Zhu, Chunbo Yuan, Lijie Zhao, Suhua Institute of Remote Sensing Satellite CAST Beijing China Image Processing Center School of Astroautics Beihang University Beijing China Tianjin Zhuocheng Technology Development Co. LTD. Tianjin China China Shield Tunnelling Engineering Co. Ltd. Tianjin China

ISBN: (纸本)9789819975488

Synthetic Aperture Radar (SAR) images are valuable assets in remote sensing and earth observation due to the imaging ability in all-weather and all-time scenarios. However, acquiring and labeling SAR images can be challenging and expensive. Therefore, the domain adaptation (DA) from easily available optical image data to SAR image data is a significant motivation in SAR image interpretation. In this paper, a novel cross-modal DA approach from optical to SAR is introduced, namely CDA-SAR. It is pre-trained on easily obtained optical remote sensing images and achieves cross-modal instance segmentation in SAR images via unsupervised domain adaptation (UDA). The proposed UDA method contains sample-level and feature-level adaptation strategies to perform style-transferring and feature alignment in the latent space, respectively. The proposed domain adaptive approach is validated to be universal to various CNN-based instance segmentation models. Experimental results prove CDA-SAR achieves more than 280% performance improvement on HRSID dataset than that without DA. Furthermore, it shows robustness in complex background interference and multi-scale object distribution scenes in SAR images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

Assessing underlying pulsatile structures with laparoscopic tools using proximal vibroacoustic sensing

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Current Directions in Biomedical Engineering 2024年第1期10卷 37-40页

作者： Kaabachi, Syrine Illanes, Alfredo Esmaeili, Nazila Sühn, Thomas Spiller, Moritz Friebe, Michael Hansen, Christian Boese, Axel INKA-Innovation Laboratory for Image Guided Therapy Otto-von-Guericke University Magdeburg Germany SURAG Medical GmbH Leipzig Germany Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Munich Germany AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland CIBE - Center of Innovation Business Development & Entrepreneurship FOM University of Applied Sciences Essen Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany

This work presents a novel approach for detecting underlying pulsatile structures with laparoscopic tools by using proximal vibroacoustic sensing. This new method could detect pulsations at a depth of up to 1.5 cm. The outcome of this work indicates the potential of identifying deep-seated pulsatile structures during robot-assisted laparoscopy, where haptic feedback is highly limited, to help surgeons prevent vascular damage that may lead to injuries. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Laparoscopy

Lung Segmentation Using ResUnet++ Powered by Variational Auto Encoder-Based Enhancement in Chest X-ray images 26th

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Lung Segmentation Using ResUnet++ Powered by Variational Aut...

26th Annual Conference on Medical image Understanding and Analysis, MIUA 2022

作者： Ibrahim, Samar Elgohary, Kareem Higazy, Mahmoud Mohannad, Thanaa Selim, Sahar Elattar, Mustafa Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza Egypt School of Information Technology and Computer Science Nile University Giza Egypt Research and Development Division Intixel Co. S.A.E. Cairo Egypt

ISBN: (纸本)9783031120527

X-ray has a huge popularity around the world. This is due to its low cost and easy to access. Most of lung diseases are diagnosed using Chest X-ray (CXR). So, developing computer aided detection (CAD) provided with automatic lung segmentation can improve the efficiency of the detection and support the physicians to make a reliable decision at early stages. But when the input image has image artifacts, then any lung segmentation model will introduce suboptimal lung segmentation results. In this paper, a new approach is proposed to make the lung segmentation model robust and boost the basic models’ performance. This is done through two stages: image enhancement and lung segmentation. The first stage aims to enhance the image quality by using the combination of Variational Autoencoder (VAE) and U-Net. The features of VAE and U-Net are concatenated to decode the enhanced output CXR image. The second stage is segmenting the lung using ResUNet++, which can perform well with a few number of images. Moreover, it combines the advantages of residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP), and attention blocks. The proposed method is trained on JSRT dataset. The proposed approach achieved Dice score of 0.9848, 0.99 and the Jaccard score of 0.9783, 0.984 on test data of NLM-MC and JSRT datasets, respectively, which outperforms results in other state of the art models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS 2023)

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IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital...

2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023

作者： Cannavò, Alberto Kapralos, Bill Seinfeld, Sofia Pratticò, Filippo Gabriele Zhang, Congyi Politecnico di Torino Turin Italy Game Development and Interactive Media Program and MaxSIMhealth Lab Ontario Tech University Oshawa Canada Image Processing and Multimedia Technology Center Universitat Politècnica de Catalunya-Barcelona Tech Terrassa Spain Politecnico di Torino Turin Italy Department of Computer Science The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350348392

Today, Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) technologies are becoming fundamental across a wide variety of application domains. All these technologies fall under the Extended Reality (XR) spectrum. Areas in which XR technologies and, in particular, immersive experiences have been successfully introduced are expanding daily, from traditional areas, such as entertainment and video game production, to novel scenarios, such as industry, healthcare, smart cities, and autonomous vehicles, to mention a few. The spread of XR and VR is also driven by technological advancements that are making new ways of operating within virtual experiences easier, and sometimes even possible. In particular, the availability of new hardware and software tools make the use of '3D reconstructions', 'digital twins' and 'simulations' commonplace in many XR and VR applications. For example, the possibility to leverage networks with higher bandwidth than never before and lower latency makes it possible to transmit the amount of data needed for massive, real-time digital twins, whereas high-performance hardware enables the reconstruction of high-fidelity digital objects and environments, while lowering the time needed for high-demanding simulations. The aim of the 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS) workshop is to bring together researchers, practitioners, educators, and students to share ideas and promote further work in this growing area of 3D reconstruction, digital twinning, and simulation in VR and related technologies. © 2023 IEEE.

关键词： Augmented reality

FEATURE-ADAPTIVE INTERACTIVE THRESHOLDING OF LARGE 3D VOLUMES

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arXiv 2022年

作者： Lang, Thomas Sauer, Tomas FORWISS University of Passau Fraunhofer Institute of Integrated Circuits Division Development Center X-ray Technology Germany Digital Image Processing FORWISS University of Passau Fraunhofer Institute of Integrated Circuits Division Development Center X-ray Technology Germany

Thresholding is the most widely used segmentation method in volumetric image processing, and its pointwise nature makes it attractive for the fast handling of large three-dimensional samples. However, global thresholds often do not properly extract components in the presence of artifacts, measurement noise or grayscale value fluctuations. This paper introduces Feature-Adaptive Interactive Thresholding (FAITH), a thresholding technique that incorporates (geometric) features, local processing and interactive user input to overcome these limitations. Given a global threshold suitable for most regions, FAITH uses interactively selected seed voxels to identify critical regions in which that threshold will be adapted locally on the basis of features computed from local environments around these voxels. The combination of domain expert knowledge and a rigorous mathematical model thus enables a very flexible way of local thresholding with intuitive user interaction. A qualitative analysis shows that the proposed model is able to overcome limitations typically occuring in plain thresholding while staying efficient enough to also allow the segmentation of big *** Codes 68U10 © 2022, CC BY.

关键词： image segmentation

MindEye-OmniAssist: A Gaze-Driven LLM-Enhanced Assistive Robot System for Implicit Intention Recognition and Task Execution

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arXiv 2025年

作者： Zhang, Zejia Yang, Bo Chen, Xinxing Shi, Weizhuang Wang, Haoyuan Luo, Wei Huang, Jian Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China State key laboratory of intelligent vehicle safety technology chongqing changan automobile co ltd Chongqing400023 China Science and technology innovation center China ship development and design centre Wuhan430060 China

A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. Moreover, the restricted intent recognition capability constrains the assistive system’s ability to provide diverse assistance functions. In this paper, we propose an open implicit intention recognition framework powered by Large Language Model (LLM) and Vision Foundation Model (VFM), which can process gaze input and recognize user intents that are not confined to predefined or specific scenarios. Furthermore, we implement a gaze-driven LLM-enhanced assistive robot system (MindEye-OmniAssist) that recognizes user’s intentions through gaze and assists in completing task. To achieve this, the system utilizes open vocabulary object detector, intention recognition network and LLM to infer their full intentions. By integrating eye movement feedback and LLM, it generates action sequences to assist the user in completing tasks. Real-world experiments have been conducted for assistive tasks, and the system achieved an overall success rate of 41/55 across various undefined tasks. Preliminary results show that the proposed method holds the potential to provide a more user-friendly human-computer interaction interface and significantly enhance the versatility and effectiveness of assistive systems by supporting more complex and diverse task. Copyright © 2025, The Authors. All rights reserved.

关键词： Eye movements

Haar Wavelets, Gradients and Approximate TV Regularization

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arXiv 2022年

作者： Sauer, Tomas Stock, Andreas Michael Chair of Mathematical Image Processing University of Passau Innstr. 43 Passau94032 Germany Development Center X-ray Technology Fraunhofer Institute of Integrated Circuits Innstr. 43 Passau94032 Germany FORWISS University of Passau Innstr. 43 Passau94032 Germany

image denoising by means of total variation (TV) regularization is still a standard procedure. For very large images, especially three-dimensional voxel datasets, however, this can be computationally infeasible. We show how this TV regularization can be approximately performed even in arbitrary dimensions by applying appropriate shrinkage to selected and properly weighted Haar wavelet coefficients, all of which depends even on the dimensionality of the data. Our approach acts entirely on the wavelet coefficients which represent the compressed image, and is therefore suited for the application on large three-dimensional images represented in the Haar wavelet basis, e.g., volumes from computed *** Codes 68Q25, 68R10, 68U05 Copyright © 2022, The Authors. All rights reserved.

关键词： image denoising

Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

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arXiv 2024年

作者： Yang, Jing Cheng, Jian Li, Cheng Fan, Wenxin Zou, Juan Wu, Ruoyou Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Peng Cheng Laboratory Guangdong Shenzhen China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Yuhang District Hangzhou China

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Key-Point Based Automated Diagnosis for Alveolar Dehiscence in Mandibular Incisors Using Convolutional Neural Network

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SSRN

SSRN 2022年

作者： Liu, Tianyu Ye, Yingzhi Liu, Chengcheng Chen, Jing Liu, Yuehua Xing, Wenyu Ta, Dean Academy for Engineering and Technology Fudan University Shanghai200433 China Department of Orthodontics Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University Shanghai200001 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China Center for Medical Engineering School of Information Science and Technology Fudan University Shanghai200438 China

The aim of this study was to propose an automated diagnosis method for alveolar dehiscence in the anterior teeth using convolutional neural network (CNN).The Cone-beam computed tomography (CBCT) scanning was performed on 387 orthodontic patients at Shanghai Stomatological Hospital. A total of 1017 mandibular incisors with the largest labiolingual sectional images were obtained from the CBCT data. We proposed two strategies of automated diagnosis methods for alveolar dehiscence. The first strategy (the binary classification method (BCM)) was to take the task directly as a classic binary classification. The second strategy (the key-point based method (KPM)) was to use the CNN to search two key points (i.e., the cement-enamel junction (CEJ) and the alveolar crest (AC)) firstly and then make a diagnosis according to the distance between the two key points. In both CNN strategies, 90% of the mandibular incisor images were assigned to the training dataset, and the rest 10% were assigned to the testing *** BCM showed limited performance in the diagnosis of alveolar dehiscence, with diagnostic accuracy below 70% for all the five classification networks. The KPM showed superior diagnostic performance with an accuracy of 90.9%, a sensitivity of 81.4% and a specificity of 94.4%, respectively, in the testing *** results proved that the diagnosis of alveolar dehiscence could be realized using CNN and the proposed KPM have the advantage of high accuracy and real-time performance. This study suggests that the KPM might have the potential for the diagnosis of alveolar dehiscence in the clinic. © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks