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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是101-110 订阅

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CT-Guided, Unsupervised Super-Resolution Reconstruction of Single 3D Magnetic Resonance image 26th

CT-Guided, Unsupervised Super-Resolution Reconstruction of S...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Wang, Jiale Heimann, Alexander F. Tannast, Moritz Zheng, Guoyan Shanghai Jiao Tong Univ Inst Med Robot Sch Biomed Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Univ Fribourg HFR Cantonal Hosp Dept Orthopaed Surg Fribourg Switzerland

ISBN: (纸本)9783031439063;9783031439070

Deep learning-based algorithms for single MR image (MRI) super-resolution have shown great potential in enhancing the resolution of low-quality images. However, many of these methods rely on supervised training with paired low-resolution (LR) and high-resolution (HR) MR images, which can be difficult to obtain in clinical settings. This is because acquiring HR MR images in clinical settings requires a significant amount of time. In contrast, HR CT images are acquired in clinical routine. In this paper, we propose a CT-guided, unsupervised MRI super-resolution reconstruction method based on joint cross-modality image translation and super-resolution reconstruction, eliminating the requirement of high-resolution MRI for training. The proposed approach is validated on two datasets respectively acquired from two different clinical sites. Well-established metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), and Learned Perceptual image Patch Similarity (LPIPS) are used to assess the performance of the proposed method. Our method achieved an average PSNR of 32.23, an average SSIM of 0.90 and an average LPIPS of 0.14 when evaluated on data of the first site. An average PSNR of 30.58, an average SSIM of 0.88, and an average LPIPS of 0.10 were achieved by our method when evaluated on data of the second site.

关键词： Unsupervised image super-resolution Cross-modality image translation CT-Guided Magnetic resonance imaging

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A Deep Learning-Cryptography Hybrid Approach for Ensuring medical image Confidentiality 3

A Deep Learning-Cryptography Hybrid Approach for Ensuring Me...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Chinta, Hitesh Surya Sai, Maganti Akshita Uma Kavitha, C.R. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350369083

This paper addresses the important need for security measures in the transmission of medical images to ensure the confidentiality of sensitive patient information. Leveraging a synergistic combination of advanced cryptography and steganography techniques, including DNA-based AES encryption and LSB embedding, the proposed system ensures the secure hiding and transmission of patient data. Furthermore, Discrete Wavelet Transform compression is employed to optimize data size while preserving integrity and also applied deep learning models to the HAM10000 dataset. Experimental evaluations prove the superiority and efficacy of the suggested strategy in safeguarding medical data against unauthorized access and ensuring confidentiality. This work contributes significantly to the advancement of secure medical data transmission systems, addressing security concerns and regulatory requirements in the healthcare domain. © 2024 IEEE.

关键词： Steganography

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Incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts 3rd

Incremental Augmentation Strategies for Personalised Continu...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Patra, Arijit UCB SloughSL1 3WE United Kingdom

ISBN: (纸本)9783031820069

Like most connectionist systems, deep networks have been found to be prone to catastrophic forgetting effects. This makes generalization of deep neural network pipelines a challenge as new additions to prediction requirements at runtime would invariably require retraining on not only the new dataset, but also substantial portions of older task data. This is a difficult task in personalised clinical imaging where retention of datasets over extended time is challenged by the fact that the already limited individual-specific examples may not be stored over extended time owing to memory, legal and infrastructure constraints. Thus, there is a need to rethink the process of deploying learning pipelines in personalised healthcare contexts that address forgetting as part of initial and incremental task learning over time. This has been modelled as an incremental learning problem, with an evolving interest in exploring the applicability of such a paradigm in precision medicine contexts. We propose a novel approach to the incremental class addition problem for individualised data curation settings pertinent to personalised medicine, where a retention of limited numbers of exemplars of old classes helps reduce forgetting instead of large-scale data storage, using a strategy of incremental time sample augmentation with fractional linear transformations and weighted knowledge distillation objectives to correct for evolving class imbalance effects in conjunction with the mitigation of reductions in initial task performances following incremental adaptation to new data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Personalized medicine

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Cross-Adversarial Local Distribution Regularization for Semi-supervised medical image Segmentation 26th

Cross-Adversarial Local Distribution Regularization for Semi...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Thanh Nguyen-Duc Trung Le Bammer, Roland Zhao, He Cai, Jianfei Dinh Phung Monash Univ Melbourne Australia CSIROs Data61 Melbourne Australia

ISBN: (纸本)9783031439063;9783031439070

medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data. Existing semi-supervised segmentation methods are usually based on the smoothness assumption. This assumption implies that the model output distributions of two similar data samples are encouraged to be invariant. In other words, the smoothness assumption states that similar samples (e.g., adding small perturbations to an image) should have similar outputs. In this paper, we introduce a novel cross-adversarial local distribution (Cross-ALD) regularization to further enhance the smoothness assumption for semi-supervised medical image segmentation task. We conducted comprehensive experiments that the Cross-ALD archives state-of-the-art performance against many recent methods on the public LA and ACDC datasets.

关键词： Semi-supervised segmentation Adversarial local distribution Adversarial examples Cross-adversarial local distribution

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A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery 9th

A Review of 3D Reconstruction Techniques for Deformable Tiss...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Xu, Mengya Guo, Ziqi Wang, An Bai, Long Ren, Hongliang Chinese Univ Hong Kong CUHK Dept Elect Engn Sha Tin Hong Kong Peoples R China CUHK Shenzhen Res Inst Shenzhen Peoples R China Natl Univ Singapore Dept Biomed Engn Singapore Singapore

ISBN: (纸本)9783031776090;9783031776106

As a crucial and intricate task in robotic minimally invasive surgery, reconstructing surgical scenes using stereo or monocular endoscopic video holds immense potential for clinical applications. NeRF-based techniques have recently garnered attention for the ability to reconstruct scenes implicitly. On the other hand, Gaussian splatting-based 3D-GS represents scenes explicitly using 3D Gaussians and projects them onto a 2D plane as a replacement for the complex volume rendering in NeRF. However, these methods face challenges regarding surgical scene reconstruction, such as slow inference, dynamic scenes, and surgical tool occlusion. This work explores and reviews state-of-the-art (SOTA) approaches, discussing their innovations and implementation principles. Furthermore, we replicate the models and conduct testing and evaluation on two datasets. The test results demonstrate that with advancements in these techniques, achieving real-time, high-quality reconstructions becomes feasible. The code is available at: https://***/Epsilon404/ surgicalnerf.

关键词： Volume rendering

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Deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver 3rd

Deep Learning for Resolving 3D Microstructural Changes in ...

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作者： Laprade, William M. Pirzamanebin, Behnaz Mokso, Rajmund Nilsson, Julia Dahl, Vedrana A. Dahl, Anders B. Holmberg, Dan Schmidt-Christensen, Anja Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Quantification of Imaging Data from MAX IV DTU Kongens Lyngby Denmark Department of Statistics Lund University Lund Sweden Department of Physics Technical University of Denmark Kgs. Lyngby Denmark Laboratory Medicine University of California San Francisco San Francisco United States Department of Experimental Medical Sciences Lund University Malmö Sweden Department of Medical Biosciences Umeå University Umeå Sweden Lund University Diabetes Center Lund University Malmö Sweden

ISBN: (纸本)9783031820069

Portal hypertension, a life-threatening complication of cirrhosis, is largely triggered by increased intrahepatic vascular resistance. Fibrosis, regenerative nodule formation, intrahepatic angiogenisis and sinusoidal remodelling are classical mechanisms that account for increased intrahepatic vascular resistance in cirrhosis. Our study leverages high-resolution 3D synchrotron radiation-based microtomography and a deep learning-based segmentation approach to investigate these microstructural changes in the liver. By employing a multi-planar U-Net model, trained using annotated tomographic slices sourced from our developed online learning tool, we effectively quantify critical vascular parameters such as sinusoid proportions, local thickness, and connectivity. These insights advance our understanding of liver microarchitecture and also allows correlating vascular parameters to inflammation and fibrosis severity. Understanding and quantifying these microstructural changes is essential to be able to predict the transition from seemingly benign conditions like steatosis or mild inflammation to severe fibrosis and cirrhosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Pathology

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A Vision Transformer with Adaptive Cross-image and Cross-Resolution Attention 9th

A Vision Transformer with Adaptive Cross-Image and Cross-Res...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Murray, Benjamin A. K. Tan, Wei R. Canas, Liane S. Smith, Catherine H. Mahil, Satveer K. Ourselin, Sebastien Modat, Marc Kings Coll London Biomed Engn & Image Sci London SE1 7EH England Guys & St Thomas NHS Fdn Trust St Johns Inst Dermatol London England Kings Coll London London England

ISBN: (纸本)9783031776090;9783031776106

Vision Transformers (ViTs) are the current state-of-the-art in deep learning for computer vision tasks. They are trained on vast datasets and are capable of useful downstream tasks through clever use of the attention mechanism. The biggest limiting factor for ViTs is the number of pixels and tokens that can be processed in a given pass. Memory constraints on both patch size and the number of patches mean that ViTs are most effective at processing relatively low-resolution images. Whilst ViTs can attend very flexibly across an image, attending across images in a naive fashion requires memory proportional to the square of the number of images. This is a further limiting factor. Given the task of automated assessment of psoriasis severity, a chronic skin condition that can affect large portions of a person's skin, it is necessary to look across multiple images and at fine detail in large images. We present a method that adapts ViTs to a two-stage design that allows for the regression of a patient's psoriasis score across multiple images and resolutions and shows its effectiveness relative to a baseline ViT. The implementation of our method is available at https://***/KCL-BMEIS/***.

关键词： Vision Transformer Multi-image Multi-resolution Psoriasis

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Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Lin, Zudi Wei, Donglai Gupta, Aarush Liu, Xingyu Sun, Deqing Pfister, Hanspeter Harvard Univ Cambridge MA 02138 USA Boston Coll Chestnut Hill MA 02167 USA CMU Pittsburgh PA USA Google Res Mountain View CA USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Objects with complex structures pose significant challenges to existing instance segmentation methods that rely on boundary or affinity maps, which are vulnerable to small errors around contacting pixels that cause noticeable connectivity change. While the distance transform (DT) makes instance interiors and boundaries more distinguishable, it tends to overlook the intra-object connectivity for instances with varying width and result in over-segmentation. To address these challenges, we propose a skeleton-aware distance transform (SDT) that combines the merits of object skeleton in preserving connectivity and DT in modeling geometric arrangement to represent instances with arbitrary structures. Comprehensive experiments on histopathology image segmentation demonstrate that SDT achieves state-of-the-art performance.

关键词： image segmentation

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Self-adaptive Adversarial Training for Robust medical Segmentation 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Wang, Fu Fu, Zeyu Zhang, Yanghao Ruan, Wenjie Univ Exeter Exeter EX4 4QF Devon England Univ Liverpool Liverpool L69 3BX Merseyside England

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Adversarial training has been demonstrated to be one of the most effective approaches to training deep neural networks that are robust to malicious perturbations. Research on effectively applying it to produce robust 3D medical image segmentation models is ongoing. While few empirical studies have been done in this area, developing effective adversarial training methods for complex segmentation models and high-volume 3D examples is challenging and requires theoretical support. In this paper, we consider the robustness of 3D segmentation tasks from a PAC-Bayes generalisation perceptive and show that reducing the trained models' Lipschitz constant benefits the models' robustness performance. Demonstrating by empirical investigation, we show that adjusting the adversarial iteration can help to reduce the model's Lipschitz constant, enabling a self-adaptive adversarial training strategy. Empirical studies on the medical segmentation decathlon dataset have been done to demonstrate the efficiency of the proposed adversarial training method. Our implementation is available at https://***/TrustAI/SEAT.

关键词： medical image Segmentation Adversarial Training

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Self-aware and Cross-Sample Prototypical Learning for Semi-supervised medical image Segmentation 26th

Self-aware and Cross-Sample Prototypical Learning for Semi-s...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhang, Zhenxi Ran, Ran Tian, Chunna Zhou, Heng Li, Xin Yang, Fan Jiao, Zhicheng Xidian Univ 2 South Taibai Rd Xian Shanxi Peoples R China Xi An Jiao Tong Univ Ctr Canc Affiliated Hosp 1 Xian Peoples R China AIQ Abu Dhabi U Arab Emirates Brown Univ Warren Alpert Med Sch Dept Diagnost Imaging Providence RI 02912 USA

ISBN: (纸本)9783031438943;9783031438950

Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs. Furthermore, we introduce a self-aware consistency learningmethodwhich exploits unlabeled data to improve the compactness of pseudo labels within each class. Moreover, a dual loss re-weightingmethod is integrated into the cross-sample prototypical consistency learningmethod to improve the reliability and stability of ourmodel. Extensive experiments onACDCdataset and PROMISE12 dataset validate that SCP-Net outperforms other state-of-the-art semi-supervised segmentation methods and achieves significant performance gains compared to the limited supervised training. Code is available at https://***/Medsemiseg/SCP- Net.

关键词： Prototypical learning Consistency learning Semi-supervised segmentation

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