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

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ModeT: Learning Deformable image Registration via Motion Decomposition Transformer 26th

ModeT: Learning Deformable Image Registration via Motion Dec...

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

作者： Wang, Haiqiao Ni, Dong Wang, Yi Shenzhen Univ Smart Med Imaging Learning & Engn SMILE Lab Med UltraSound Image Comp MUSIC Lab Sch Biomed EngnMed Sch Shenzhen Peoples R China

ISBN: (纸本)9783031439988;9783031439995

the Transformer structures have been widely used in computer vision and have recently made an impact in the area of medical image registration. However, the use of Transformer in most registration networks is straightforward. these networks often merely use the attention mechanism to boost the feature learning as the segmentation networks do, but do not sufficiently design to be adapted for the registration task. In this paper, we propose a novel motion decomposition Transformer (ModeT) to explicitly model multiple motion modalities by fully exploiting the intrinsic capability of the Transformer structure for deformation estimation. the proposed ModeT naturally transforms the multi-head neighborhood attention relationship into the multi-coordinate relationship to model multiple motion modes. then the competitive weighting module (CWM) fuses multiple deformation sub-fields to generate the resulting deformation field. Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets show that our method outperforms current state-of-the-art registration networks and Transformers, demonstrating the potential of our ModeT for the challenging non-rigid deformation estimation problem. the benchmarks and our code are publicly available at https://***/ZAX130/SmileCode.

关键词： Deformable image registration Motion decomposition Transformer Attention Pyramid structure

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Multi-scale Prototypical Transformer forWhole Slide image Classification 26th

Multi-scale Prototypical Transformer forWhole Slide Image Cl...

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

作者： Ding, Saisai Wang, Jun Li, Juncheng Shi, Jun Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains challenging due to the extreme imbalance between the positive and negative instances in bags, and the complicated pre-processing to fuse multi-scale information ofWSI. To this end, we propose a novel multi-scale prototypical Transformer (MSPT) for WSI classification, which includes a prototypical Transformer (PT) module and a multi-scale feature fusion module (MFFM). the PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture. It substitutes all instances with cluster prototypes, which are then re-calibrated through the self-attention mechanism of Transformer. thereafter, an MFFM is proposed to fuse the clustered prototypes of different scales, which employs MLP-Mixer to enhance the information communication between prototypes. the experimental results on two public WSI datasets demonstrate that the pro-posed MSPT outperforms all the compared algorithms, suggesting its potential applications.

关键词： Whole slide image Multiple instance learning Multi-scale feature Prototypical Transformer

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RESToring Clarity: Unpaired Retina image Enhancement Using Scattering Transform 26th

RESToring Clarity: Unpaired Retina Image Enhancement Using S...

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

作者： Oh, Ellen Jieun Hwang, Yechan Han, Yubin Choi, Taegeun Lee, Geunyoung Kim, Won Hwa Pohang Univ Sci & Technol POSTECH Pohang South Korea Mediwhale Seoul South Korea

ISBN: (纸本)9783031439988;9783031439995

Retina images are non-invasive and highly effective in the diagnosis of various diseases such as cardiovascular and ophthalmological diseases. Accurate diagnosis depends on the quality of the retina images, however, obtaining high-quality images can be challenging due to various factors, such as noise, artifacts, and eyemovement. Methods for enhancing retina images are therefore in high demand for clinical purposes, yet the problem remains challenging as there is a natural trade-off between preserving anatomical details (e.g., vessels) and increasing overall image quality other than the content in it. Moreover, training an enhancement model often requires paired images that map low-quality images to high-quality images, which may not be available in practice. In this regime, we propose a novel Retina image Enhancement framework using ScatteringTransform (REST). REST uses unpaired retina image sets and does not require prior knowledge of the degraded factors. the generator in REST enhances retina images by utilizing the Anatomy Preserving Branch (APB) and the Tone Transferring Branch (TTB) with different roles. Our model successfully enhances low-quality retina images demonstrating commendable results on two independent datasets.

关键词： image enhancement

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PIViT: Large Deformation image Registration with Pyramid-Iterative Vision Transformer 26th

PIViT: Large Deformation Image Registration with Pyramid-Ite...

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

作者： Ma, Tai Dai, Xinru Zhang, Suwei Wen, Ying East China Normal Univ Sch Commun & Elect Engn Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Large deformation image registration is a challenging task in medical image registration. Iterative registration and pyramid registration are two common CNN-based methods for the task. However, these methods usually consume more parameters and time. Additionally, the existing CNN-based registration methods mainly focus on local feature extraction, limiting their ability to capture the long-distance correlation between image pairs. In this paper, we propose a fast and accurate learning-based algorithm, Pyramid-Iterative Vision Transformer (PIViT), for 3D large deformation medical image registration. Our method constructs a novel pyramid iterative composite structure to solve large deformation problem by using low-scale iterative registration with a Swin Transformer-based long-distance correlation decoder. Furthermore, we exploit pyramid structure to supplement the detailed information of the deformation field by using high-scale feature maps. Comprehensive experimental results implemented on brain MRI and liver CT datasets show that the proposed method is superior to the existing registration methods in terms of registration accuracy, training time and parameters, especially of a significant advantage in running time. Our code is available at https://***/Torbjorn1997/PIViT.

关键词： medical image registration convolutional neural networks image processing

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Conditional Diffusion Models for Weakly Supervised medical image Segmentation 26th

Conditional Diffusion Models for Weakly Supervised Medical I...

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

作者： Hu, Xinrong Chen, Yu-Jen Ho, Tsung-Yi Shi, Yiyu Univ Notre Dame Notre Dame IN 46556 USA Natl Tsing Hua Univ Hsinchu Taiwan Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. Observing that conditional diffusion models (CDM) is capable of generating images subject to specific distributions, in this work, we utilize category-aware semantic information underlied in CDM to get the prediction mask of the target object with only image-level annotations. More specifically, we locate the desired class by approximating the derivative of the output of CDM w.r.t the input condition. Our method is different from previous diffusion model methods with guidance from an external classifier, which accumulates noises in the background during the reconstruction process. Our method outperforms state-of-the-art CAM and diffusion model methods on two public medical image segmentation datasets, which demonstrates that CDM is a promising tool in WSSS. Also, experiment shows our method is more time-efficient than existing diffusion model methods, making it practical for wider applications. the codes are available at https://***/xhu248/cond_ddpm_wsss.

关键词： weakly supervised semantic segmentation diffusion models brain tumor magnetic resonance imaging

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Enhancing Foundation Model Robustness for Multi-center Real-World medical image Analysis 1st

Enhancing Foundation Model Robustness for Multi-center Real...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World medical images, CARE 2024 Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Huang, Jinlong Sun, Xiao Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031870088

Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse datasets remains untapped in real-world scenarios. Our study harnesses SAM’s capabilities to address the segmentation intricacies of clinically crucial, yet challenging, features in complex medical imaging. To bridge this gap, we embark on a comprehensive exploration anchored in Foundation models including SegVol and MedSAM, targeting three pivotal challenges: 1) enhancing the generalization of models across diverse medical image modalities and sequences, 2) reinforcing the stability of interactive segmentation, and 3) overcoming the segmentation hurdles posed by minuscule, atypical ROIs. We employ a coarse-to-fine segmentation strategy, using the CARE24 dataset alongside a meticulously publicly available dataset to fine-tune these models for enhanced real-world performance. To boost the stability and precision of interactive segmentation, we devise a retest mechanism, ensuring consistent outcomes in user-guided processes. Furthermore, recognizing the potential of multimodal data, we introduce a fusion strategy, capitalizing on complementary information to improve overall segmentation accuracy and adaptability. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Complicated ROIs Foundation model Multi-center Segment Anything Model (SAM)

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Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis

Physics-Informed Latent Diffusion for Multimodal Brain MRI ...

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Workshop on Longitudinal Disease Tracking and Modeling with medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Lüpke, Sven Yeganeh, Yousef Adeli, Ehsan Navab, Nassir Farshad, Azade Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Stanford University Stanford United States

ISBN: (纸本)9783031845246

Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets limits the general applicability of the synthetic data they produce. To address this, we present a novel physics-informed generative model capable of synthesizing a variable number of brain MRI modalities, including those not present in the original dataset. Our approach utilizes latent diffusion models and a two-step generative process: first, unobserved physical tissue property maps are synthesized using a latent diffusion model, and then these maps are combined with a physical signal model to generate the final MRI scan. Our experiments demonstrate the efficacy of this approach in generating unseen MR contrasts and preserving physical plausibility. Furthermore, we validate the distributions of generated tissue properties by comparing them to those measured in real brain tissue. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image denoising

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Semi-supervised Domain Adaptive medical image Segmentation through Consistency Regularized Disentangled Contrastive Learning 26th

Semi-supervised Domain Adaptive Medical Image Segmentation T...

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

作者： Basak, Hritam Yin, Zhaozheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

ISBN: (纸本)9783031439001;9783031439018

Although unsupervised domain adaptation (UDA) is a promising direction to alleviate domain shift, they fall short of their supervised counterparts. In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially. Specifically, we propose a two-stage training process. First, an encoder is pre-trained in a self-learning paradigm using a novel domain-content disentangled contrastive learning (CL) along with a pixel-level feature consistency constraint. the proposed CL enforces the encoder to learn discriminative content-specific but domain-invariant semantics on a global scale from the source and target images, whereas consistency regularization enforces the mining of local pixel-level information by maintaining spatial sensitivity. this pre-trained encoder, along with a decoder, is further fine-tuned for the downstream task, (i.e. pixel-level segmentation) using a semi-supervised setting. Furthermore, we experimentally validate that our proposed method can easily be extended for UDA settings, adding to the superiority of the proposed strategy. Upon evaluation on two domain adaptive image segmentation tasks, our proposed method outperforms the SoTA methods, both in SSDA and UDA settings. Code is available at Github.

关键词： Contrastive Learning Style-content disentanglement Consistency Regularization Domain Adaptation Segmentation

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Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation 3rd

Importance of Aligning Training Strategy with Evaluation for...

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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： Fu, Yunguan Li, Yiwen Saeed, Shaheer U. Clarkson, Matthew J. Hu, Yipeng UCL London England InstaDeep London England Univ Oxford Oxford England

ISBN: (纸本)9783031537660;9783031537677

Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits from the 3D formulation. In this work, we studied the DDPM-based segmentation model for 3D multiclass segmentation on two large multiclass data sets (prostate MR and abdominal CT). We observed that the difference between training and test methods led to inferior performance for existing DDPM methods. To mitigate the inconsistency, we proposed a recycling method which generated corrupted masks based on the model's prediction at a previous time step instead of using ground truth. the proposed method achieved statistically significantly improved performance compared to existing DDPMs, independent of a number of other techniques for reducing train-test discrepancy, including performing mask prediction, using Dice loss, and reducing the number of diffusion time steps during training. the performance of diffusion models was also competitive and visually similar to non-diffusion-based U-net, within the same compute budget. the JAX-based diffusion framework has been released at https:// ***/mathpluscode/ImgX-DiffSeg.

关键词： image Segmentation Diffusion Model Prostate MR Abdominal CT

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A2FSeg: Adaptive Multi-modal Fusion Network for medical image Segmentation 26th

A2FSeg: Adaptive Multi-modal Fusion Network for Medical Imag...

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

作者： Wang, Zirui Hong, Yi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Magnetic Resonance Imaging (MRI) plays an important role in multi-modal brain tumor segmentation. However, missing modality is very common in clinical diagnosis, which will lead to severe segmentation performance degradation. In this paper, we propose a simple adaptive multi-modal fusion network for brain tumor segmentation, which has two stages of feature fusion, including a simple average fusion and an adaptive fusion based on an attention mechanism. Both fusion techniques are capable to handle the missing modality situation and contribute to the improvement of segmentation results, especially the adaptive one. We evaluate our method on the BraTS2020 dataset, achieving the state-of-the-art performance for the incomplete multi-modal brain tumor segmentation, compared to four recent methods. Our A2FSeg (Average and Adaptive Fusion Segmentation network) is simple yet effective and has the capability of handling any number of image modalities for incomplete multi-modal segmentation. Our source code is online and available at https://***/Zirui0623/***.

关键词： Modality-adaptive fusion Missing modality Brain tumor segmentation Incomplete multi-modal segmentation

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