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检索条件"任意字段=6th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Co-learning Semantic-Aware Unsupervised Segmentation for Pathological image Registration 26th

Co-learning Semantic-Aware Unsupervised Segmentation for Pat...

引用

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

作者： Liu, Yang Gu, Shi Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9783031439988;9783031439995

the registration of pathological images plays an important role in medical applications. Despite its significance, most researchers in this field primarily focus on the registration of normal tissue into normal tissue. the negative impact of focal tissue, such as the loss of spatial correspondence information and the abnormal distortion of tissue, are rarely considered. In this paper, we propose a novel unsupervised approach for pathological image registration by incorporating segmentation and inpainting. the registration, segmentation, and inpainting modules are trained simultaneously in a co-learning manner so that the segmentation of the focal area and the registration of inpainted pairs can improve collaboratively. Overall, the registration of pathological images is achieved in a completely unsupervised learning framework. Experimental results on multiple datasets, including Magnetic Resonance Imaging (MRI) of T1 sequences, demonstrate the efficacy of our proposed method. Our results show that our method can accurately achieve the registration of pathological images and identify lesions even in challenging imaging modalities. Our unsupervised approach offers a promising solution for the efficient and cost-effective registration of pathological images. Our code is available at https://***/brain-intelligence-lab/GIRNet.

关键词： Unsupervised Collaborative Learning Registration Segmentation Pathological image

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Learning Expected Appearances for Intraoperative Registration During Neurosurgery 26th

Learning Expected Appearances for Intraoperative Registratio...

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

作者： Haouchine, Nazim Dorent, Reuben Juvekar, Parikshit Torio, Erickson Wells, William M., III Kapur, Tina Golby, Alexandra J. Frisken, Sarah Harvard Med Sch Brigham & Womens Hosp Boston MA 02115 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783031439957;9783031439964

We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations. Our method estimates the camera pose by minimizing the dissimilarity between the intraoperative 2D view through the optical microscope and the synthesized expected texture. In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy. We applied our method in the context of neuronavigation during brain surgery. We evaluated our approach on synthetic data and on retrospective data from 6 clinical cases. Our method outperformed state-of-the-art methods and achieved accuracies that met current clinical standards.

关键词： Intraoperative Registration image-guided Neurosurgery Augmented Reality Neural image Analogy 3D Pose Estimation

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SegNetr: Rethinking the Local-Global Interactions and Skip Connections in U-Shaped Networks 26th

SegNetr: Rethinking the Local-Global Interactions and Skip C...

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

作者： Cheng, Junlong Gao, Chengrui Wang, Fengjie Zhu, Min Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. However, existing U-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network;2) simply fuse the features of encoder and decoder, ignoring the connection between their spatial locations. In this paper, we rethink the above problem and build a lightweight medical image segmentation network, called SegNetr. Specifically, we introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. At the same time, we design a general information retention skip connection (IRSC) to preserve the spatial location information of encoder features and achieve accurate fusion with the decoder features. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59% and 76% fewer parameters and GFLOPs than vanilla U-Net, while achieving segmentation performance comparable to state-of-the-art methods. Notably, the components proposed in this paper can be applied to other U-shaped networks to improve their segmentation performance.

关键词： Local-global interactions Information retention skip connection medical image segmentation U-shaped networks

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Dynamic Curriculum Learning via In-Domain Uncertainty for medical image Classification 26th

Dynamic Curriculum Learning via In-Domain Uncertainty for Me...

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

作者： Li, Chaoyi Li, Meng Peng, Can Lovell, Brian C. Univ Queensland Sch EECS St Lucia Qld 4072 Australia CSIRO DATA 61 Robot & Autonomous Syst Grp Pullenvale Australia

ISBN: (纸本)9783031439032;9783031439049

this paper presents an innovative approach to curriculum learning, which is a technique used to train learning models. Curriculum learning is inspired by the way humans learn, starting with simple examples and gradually progressing to more challenging ones. there are currently two main types of curriculum learning: fixed curriculum generated by transfer learning, and self-paced learning based on loss functions. However, these methods have limitations that can hinder their effectiveness. To overcome these limitations, this article proposes a new approach called Dynamic Curriculum Learning via In-Domain Uncertainty (DCLU), which is derived from uncertainty estimation. the proposed approach utilizes a Dirichlet distribution classifier to obtain prediction and uncertainty estimates from the network, which can be used as a metric to quantify the difficulty level of the data. An uncertainty-aware sampling pacing function is also introduced to adapt the curriculum according to the difficulty metric. this new approach has been evaluated on two medical image datasets, and the results show that it outperforms other curriculum learning methods. the source code for this approach will be released at https://***/Joey2117/DCLU.

关键词： medical image classification Curriculum learning Uncertainty estimation

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Investigating Data Memorization in 3D Latent Diffusion Models for medical image Synthesis 1

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention (DGM4MICCAI) at the 26th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Dar, Salman Ul Hassan Ghanaat, Arman Kahmann, Jannik Ayx, Isabelle Papavassiliu, theano Schoenberg, Stefan O. Engelhardt, Sandy Heidelberg Univ Hosp Dept Internal Med 3 Grp Artificial Intelligence CardiovascularMed D-69120 Heidelberg Germany AI Hlth Innovat Cluster Heidelberg Germany German Ctr Cardiovasc Res DZHK Partner Site Heidelberg Mannheim Heidelberg Germany Heidelberg Univ Univ Med Ctr Mannheim Dept Radiol & Nucl Med Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany Univ Med Ctr Mannheim Dept Med Cardiol 1 Theodor Kutzer Ufer 1-3 D-68167 Mannheim Germany

ISBN: (数字)9783031537677

ISBN: (纸本)9783031537660;9783031537677

Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored. Here, we assess the memorization capacity of 3D latent diffusion models on photon-counting coronary computed tomography angiography and knee magnetic resonance imaging datasets. To detect potential memorization of training samples, we utilize self-supervised models based on contrastive learning. Our results suggest that such latent diffusion models indeed memorize training data, and there is a dire need for devising strategies to mitigate memorization.

关键词： Deep generative models Latent diffusion Data memorization Patient privacy Contrastive learning

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Understanding Silent Failures in medical image Classification 1

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

作者： Bungert, Till J. Kobelke, Levin Jaeger, Paul F. German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany DKFZ Helmholtz Imaging Heidelberg Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. this can be achieved by either designing classifiers that are robust enough to avoid failures in the first place, or by detecting remaining failures using confidence scoring functions (CSFs). A predominant source of failures in image classification is distribution shifts between training data and deployment data. To understand the current state of silent failure prevention in medical imaging, we conduct the first comprehensive analysis comparing various CSFs in four biomedical tasks and a diverse range of distribution shifts. Based on the result that none of the benchmarked CSFs can reliably prevent silent failures, we conclude that a deeper understanding of the root causes of failures in the data is required. To facilitate this, we introduce SF-Visuals, an interactive analysis tool that uses latent space clustering to visualize shifts and failures. On the basis of various examples, we demonstrate how this tool can help researchers gain insight into the requirements for safe application of classification systems in the medical domain. the open-source benchmark and tool are at: https://***/IML-DKFZ/sf-visuals.

关键词： Failure detection Distribution shifts Benchmark

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Few Shot medical image Segmentation with Cross Attention Transformer 26th

Few Shot Medical Image Segmentation with Cross Attention Tra...

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

作者： Lin, Yi Chen, Yufan Cheng, Kwang-Ting Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9783031438943;9783031438950

medical image segmentation has made significant progress in recent years. Deep learning-based methods are recognized as data-hungry techniques, requiring large amounts of data with manual annotations. However, manual annotation is expensive in the field of medical image analysis, which requires domain-specific expertise. To address this challenge, few-shot learning has the potential to learn new classes from only a few examples. In this work, we propose a novel framework for few-shot medical image segme ntation, termed CAT-Net, based on cross masked attention Transformer. Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features. We further design an iterative refinement framework that refines the query image segmentation iteratively and promotes the support feature in turn. We validated the proposed method on three public datasets: Abd-CT, Abd-MRI, and Card-MRI. Experimental results demonstrate the superior performance of our method compared to state-of-the-art methods and the effectiveness of each component. Source code: https://github. com/hust- linyi/CAT- Net.

关键词： Few Shot Cross Attention Iterative Refinement

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Unsupervised Learning for Feature Extraction and Temporal Alignment of 3D+t Point Clouds of Zebrafish Embryos 26th

Unsupervised Learning for Feature Extraction and Temporal Al...

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

作者： Chen, Zhu Laube, Ina Stegmaier, Johannes Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany

ISBN: (纸本)9783031439926;9783031439933

Zebrafish are widely used in biomedical research and developmental stages of their embryos often need to be synchronized for further analysis. We present an unsupervised approach to extract descriptive features from 3D+t point clouds of zebrafish embryos and subsequently use those features to temporally align corresponding developmental stages. An autoencoder architecture is proposed to learn a descriptive representation of the point clouds and we designed a deep regression network for their temporal alignment. We achieve a high alignment accuracy with an average mismatch of only 3.83 min over an experimental duration of 5.3 h. As a fully-unsupervised approach, there is no manual labeling effort required and unlike manual analyses the method easily scales. Besides, the alignment without human annotation of the data also avoids any influence caused by subjective bias.

关键词： Embryo Development Point Clouds Unsupervised Learning Autoencoder

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Maximum Entropy on Erroneous Predictions: Improving Model Calibration for medical image Segmentation 1

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

作者： Larrazabal, Agostina J. Martinez, Cesar Dolz, Jose Ferrante, Enzo UNL CONICET Res Inst Signals Syst & Computat Intelligence SinciFICH Santa Fe Argentina ETS Montreal LIVIA Montreal PQ Canada Tryolabs Montevideo Uruguay

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Modern deep neural networks achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimates, even in situations of high uncertainty, leading to poorly calibrated and unreliable models. In this work we introduce Maximum Entropy on Erroneous Predictions (MEEP), a training strategy for segmentation networks which selectively penalizes overconfident predictions, focusing only on misclassified pixels. Our method is agnostic to the neural architecture, does not increase model complexity and can be coupled with multiple segmentation loss functions. We benchmark the proposed strategy in two challenging segmentation tasks: white matter hyperintensity lesions in magnetic resonance images (MRI) of the brain, and atrial segmentation in cardiac MRI. the experimental results demonstrate that coupling MEEP with standard segmentation losses leads to improvements not only in terms of model calibration, but also in segmentation quality.

关键词： image segmentation uncertainty calibration

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M3D-NCA: Robust 3D Segmentation with Built-In Quality Control 1

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

作者： Kalkhof, John Mukhopadhyay, Anirban Tech Univ Darmstadt Karolinenpl 5 D-64289 Darmstadt Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures. However, the real-world utility of such models is limited by their high computational requirements, which makes them impractical for resource-constrained environments such as primary care facilities and conflict zones. Furthermore, shifts in the imaging domain can render these models ineffective and even compromise patient safety if such errors go undetected. To address these challenges, we propose M3D-NCA, a novel methodology that leverages Neural Cellular Automata (NCA) segmentation for 3D medical images using n-level patchification. Moreover, we exploit the variance in M3D-NCA to develop a novel quality metric which can automatically detect errors in the segmentation process of NCAs. M3D-NCA outperforms the two magnitudes larger UNet models in hippocampus and prostate segmentation by 2% Dice and can be run on a Raspberry Pi 4 Model B (2GB RAM). this highlights the potential of M3D-NCA as an effective and efficient alternative for medical image segmentation in resource-constrained environments.

关键词： Neural Cellular Automata medical image Segmentation Automatic Quality Control

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