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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2874 条记录，以下是161-170 订阅

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Differentiable Beamforming for Ultrasound Autofocusing 26th

Differentiable Beamforming for Ultrasound Autofocusing

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

作者： Simson, Walter Zhuang, Louise Sanabria, Sergio J. Antil, Neha Dahl, Jeremy J. Hyun, Dongwoon Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9783031439988;9783031439995

Ultrasound images are distorted by phase aberration arising from local sound speed variations in the tissue, which lead to inaccurate time delays in beamforming and loss of image focus. Whereas state-of-the-art correction approaches rely on simplified physical models (e.g. phase screens), we propose a novel physics-based framework called differentiable beamforming that can be used to rapidly solve a wide range of imaging problems. We demonstrate the generalizability of differentiable beamforming by optimizing the spatial sound speed distribution in a heterogeneous imaging domain to achieve ultrasound autofocusing using a variety of physical constraints based on phase shift minimization, speckle brightness, and coherence maximization. the proposed method corrects for the effects of phase aberration in both simulation and in-vivo cases by improving image focus while simultaneously providing quantitative speed-of-sound distributions for tissue diagnostics, with accuracy improvements with respect to previously published baselines. Finally, we provide a broader discussion of applications of differentiable beamforming in other ultrasound domains.

关键词： Ultrasound image reconstruction Optimization

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Implicit Neural Representations for Joint Decomposition and Registration of Gene Expression images in the Marmoset Brain 26th

Implicit Neural Representations for Joint Decomposition and ...

引用

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

作者： Byra, Michal Poon, Charissa Shimogori, Tomomi Skibbe, Henrik RIKEN Ctr Brain Sci Brain Image Anal Unit Wako Saitama Japan Polish Acad Sci Inst Fundamental Technol Res Warsaw Poland RIKEN Ctr Brain Sci Lab Mol Mech Brain Dev Wako Saitama Japan

ISBN: (纸本)9783031439988;9783031439995

We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of brain images with similar anatomical structures, but where one image contains additional features or artifacts that are not present in the other image. To demonstrate its effectiveness, we use 2D microscopy in situ hybridization gene expression images of the marmoset brain. Accurately quantifying gene expression requires image registration to a brain template, which is difficult due to the diversity of patterns causing variations in visible anatomical brain structures. Our approach uses implicit networks in combination with an image exclusion loss to jointly perform the registration and decompose the image into a support and residual image. the support image aligns well with the template, while the residual image captures individual image characteristics that diverge from the template. In experiments, our method provided excellent results and outperformed other registration techniques.

关键词： brain deep learning gene expression implicit neural representations registration

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Asymmetric Contour Uncertainty Estimation for medical image Segmentation 1

引用

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

作者： Judge, thierry Bernard, Olivier Kim, Woo-Jin Cho Gomez, Alberto Chartsias, Agisilaos Jodoin, Pierre-Marc Univ Sherbrooke Dept Comp Sci Sherbrooke PQ Canada Ultr Ltd Oxford OX4 2SU England Univ Lyon 1 Univ Lyon CNRS CREATISInserm U1294UMR5220 Villeurbanne France

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Aleatoric uncertainty estimation is a critical step in medical image segmentation. Most techniques for estimating aleatoric uncertainty for segmentation purposes assume a Gaussian distribution over the neural network's logit value modeling the uncertainty in the predicted class. However, in many cases, such as image segmentation, there is no uncertainty about the presence of a specific structure, but rather about the precise outline of that structure. For this reason, we explicitly model the location uncertainty by redefining the conventional per-pixel segmentation task as a contour regression problem. this allows for modeling the uncertainty of contour points using a more appropriate multivariate distribution. Additionally, as contour uncertainty may be asymmetric, we use a multivariate skewed Gaussian distribution. In addition to being directly interpretable, our uncertainty estimation method outperforms previous methods on three datasets using two different image modalities. Code is available at: https://***/thierryJudge/contouring-uncertainty.

关键词： Uncertainty estimation image segmentation

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Trans-SegNet - Deep Transfer Learning Approach to Detect Abnormalities in Microscopic Blood Smear images for medical image Segmentation 5

Trans-SegNet - Deep Transfer Learning Approach to Detect Abn...

引用

5th IEEE international conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Alkhouli, Mahmoud Saed Joshi, Hiren Gujarat University Ahmedabad India

ISBN: (纸本)9798350379884

medical image segmentation is very important for detecting illness and treatment in a precise manner. the diseases like leukemia, malaria, and other blood disorders identification mainly depends on the anomalies segmentation in microscopic blood smear images. Traditional methods for medical image segmentation are laborious to unpredictability because they need a frequent domain-specific expertise and manual intervention. the study provides a novel method to segment microscopic blood smears using Trans-SegNet, a deep transfer learning algorithm. By using pre-trained Convolutional Neural Networks (CNNs), the proposed method transfer learned features efficiently to the blood smear analysis domain, so that segmentation accuracy can be improved. A pre-trained CNN is fine-tuned using blood smear images properties and for accurate segmentation, the proposed method uses a Fully Convolutional Network (FCN). Experimentation uses publicly accessible blood smear image dataset and results show that the proposed method outperforms traditional segmentation techniques. the Trans-SegNet obtained an Intersection over Union (IoU) of 89.7% and Dice Similarity Coefficient (DSC) of 92.5%. Furthermore, precision with 91.3% and recall with 90.8% were recorded. © 2024 IEEE.

关键词： Convolutional neural networks

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A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation 9th

A Federated Learning-Friendly Approach for Parameter-Efficie...

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

作者： Asokan, Mothilal Benjamin, Joseph Geo Yaqub, Mohammad Nandakumar, Karthik Mohamed bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031776090;9783031776106

Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data used for pre-training and medical (target) data. However, collecting task-specific medical data for such finetuning at a central location raises many privacy concerns. Although Federated learning (FL) provides an effective means for training on private decentralized data, communication costs in federating large foundation models can quickly become a significant bottleneck, impacting the solution's scalability. In this work, we address this problem of 'efficient communication while ensuring effective learning in FL' by combining the strengths of Parameter-Efficient Fine-tuning (PEFT) with FL. Specifically, we study plug-and-play Low-Rank Adapters (LoRA) in a federated manner to adapt the Segment Anything Model (SAM) for 3D medical image segmentation. Unlike prior works that utilize LoRA and finetune the entire decoder, we critically analyze the contribution of each granular component of SAM on finetuning performance. thus, we identify specific layers to be federated that are very efficient in terms of communication cost while producing on-par accuracy. Our experiments show that retaining the parameters of the SAM model (including most of the decoder) in their original state during adaptation is beneficial because fine-tuning on small datasets tends to distort the inherent capabilities of the underlying foundation model. On Fed-KiTS, our approach decreases communication cost (similar to 48x.) compared to full fine-tuning while increasing performance (similar to 6%. Dice score) in 3D segmentation tasks. Our approach performs similar to SAMed while achieving similar to 2.8x reduction in communication and parameters to be finetuned. We further validate our approach with experiments on Fed-IXI and Prostate MRI datasets. Our code is available at https://***/BioMedIA-MBZUAI/FLAP-SAM.

关键词： Federated Learning Foundation Model 3D medical image Segmentation Parameter-Efficient Fine-Tuning

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Input Augmentation with SAM: Boosting medical image Segmentation with Segmentation Foundation Model 26th

Input Augmentation with SAM: Boosting Medical Image Segmenta...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Zhang, Yizhe Zhou, Tao Wang, Shuo Liang, Peixian Zhang, Yejia Chen, Danny Z. Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Fudan Univ Sch Basic Med Sci Digital Med Res Ctr Shanghai 200032 Peoples R China Shanghai Key Lab MICCAI Shanghai 200032 Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9783031474002;9783031474019

the Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. this paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method.

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Intraoperative CT Augmentation for Needle-Based Liver interventions 26th

Intraoperative CT Augmentation for Needle-Based Liver Interv...

引用

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

作者： El Hadramy, Sidaty Verde, Juan Padoy, Nicolas Cotin, Stephane INRIA Strasbourg France Univ Strasbourg ICube CNRS Strasbourg France IHU Strasbourg Strasbourg France

ISBN: (纸本)9783031439957;9783031439964

this paper addresses the need for improved CT-guidance during needle-based liver procedures (i.e., tumor ablation), while reduces the need for contrast agent injection during such interventions. To achieve this objective, we augment the intraoperative CT with the preoperative vascular network deformed to match the current acquisition. First, a neural network learns local image features in a non-contrasted CT image by leveraging the known preoperative vessel tree geometry and topology extracted from a matching contrasted CT image. then, the augmented CT is generated by fusing the labeled vascular tree and the non-contrasted intraoperative CT. Our method is trained and validated on porcine data, achieving an average dice score of 0.81 on the predicted vessel tree instead of 0.51 when a medical expert segments the non-contrasted CT. In addition, vascular labels can also be transferred to provide additional information. Source code of this work is publicly available at https://***/Sidaty1/Intraoperative CT augmentation.

关键词： Liver tumor ablation Needle-based procedures Patient-specific interventions CT-guidance medical image augmentation

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FSDiffReg: Feature-Wise and Score-Wise Diffusion-Guided Unsupervised Deformable image Registration for Cardiac images 26th

FSDiffReg: Feature-Wise and Score-Wise Diffusion-Guided Unsu...

引用

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

作者： Qin, Yi Li, Xiaomeng Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Unsupervised deformable image registration is one of the challenging tasks in medical imaging. Obtaining a high-quality deformation field while preserving deformation topology remains demanding amid a series of deep-learning-based solutions. Meanwhile, the diffusion model's latent feature space shows potential in modeling the deformation semantics. To fully exploit the diffusion model's ability to guide the registration task, we present two modules: Feature-wise Diffusion-Guided Module (FDG) and Score-wise Diffusion-Guided Module (SDG). Specifically, FDG uses the diffusion model's multi-scale semantic features to guide the generation of the deformation field. SDG uses the diffusion score to guide the optimization process for preserving deformation topology with barely any additional computation. Experiment results on the 3D medical cardiac image registration task validate our model's ability to provide refined deformation fields with preserved topology effectively. Code is available at: https://***/xmed-lab/***.

关键词： Deformable image Registration Score-based Generative Model

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MAD: Modality Agnostic Distance Measure for image Registration 26th

MAD: Modality Agnostic Distance Measure for Image Registrati...

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

作者： Sideri-Lampretsa, Vasiliki Zimmer, Veronika A. Qiu, Huaqi Kaissis, Georgios Rueckert, Daniel Tech Univ Munich Munich Germany Klinkum Rechts Isar Munich Germany Helmholtz Zentrum Munich Munich Germany Imperial Coll London Dept Comp London England

ISBN: (纸本)9783031474248;9783031474255

Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between different imaging modalities, which can result in large discrepancy in image appearance. the success of multi-modal image registration, whether it is conventional or learning based, is predicated upon the choice of an appropriate distance (or similarity) measure. Particularly, deep learning registration algorithms lack in accuracy or even fail completely when attempting to register data from an "unseen" modality. In this work, we present Modality Agnostic Distance (MAD), a deep image distance measure that utilises random convolutions to learn the inherent geometry of the images while being robust to large appearance changes. Random convolutions are geometry-preserving modules which we use to simulate an infinite number of synthetic modalities alleviating the need for aligned paired data during training. We can therefore train MAD on a mono-modal dataset and successfully apply it to a multi-modal dataset. We demonstrate that not only can MAD affinely register multi-modal images successfully, but it has also a larger capture range than traditional measures such as Mutual Information and Normalised Gradient Fields. Our code is available at: https://***/ModalityAgnosticDistance/MAD.

关键词： image registration mutli-modality distance measure

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Boundary Difference over Union Loss for medical image Segmentation 26th

Boundary Difference over Union Loss for Medical Image Segmen...

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

作者： Sun, Fan Luo, Zhiming Li, Shaozi Xiamen Univ Dept Artificial Intelligence Xiamen Fujian Peoples R China

ISBN: (纸本)9783031439001;9783031439018

medical image segmentation is crucial for clinical diagnosis. However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation. those that do exist often need to be used in combination with other losses and produce ineffective results. To address this issue, we have developed a simple and effective loss called the Boundary Difference over Union Loss (Boundary DoU Loss) to guide boundary region segmentation. It is obtained by calculating the ratio of the difference set of prediction and ground truth to the union of the difference set and the partial intersection set. Our loss only relies on region calculation, making it easy to implement and training stable without needing any additional losses. Additionally, we use the target size to adaptively adjust attention applied to the boundary regions. Experimental results using UNet, TransUNet, and Swin-UNet on two datasets (ACDC and Synapse) demonstrate the effectiveness of our proposed loss function. Code is available at https://***/sunfan-bvb/BoundaryDoULoss.

关键词： medical image segmentation Boundary loss

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