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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction 26th

Dense Transformer based Enhanced Coding Network for Unsuperv...

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

作者： Xie, Wangduo Blaschko, Matthew B. Katholieke Univ Leuven Dept ESAT Ctr Proc Speech & Images Leuven Belgium

ISBN: (纸本)9783031439063;9783031439070

CT images corrupted by metal artifacts have serious negative effects on clinical diagnosis. Considering the difficulty of collecting paired data with ground truth in clinical settings, unsupervised methods for metal artifact reduction are of high interest. However, it is difficult for previous unsupervised methods to retain structural information from CT images while handling the non-local characteristics of metal artifacts. To address these challenges, we proposed a novel Dense Transformer based Enhanced Coding Network (DTEC-Net) for unsupervised metal artifact reduction. Specifically, we introduce a Hierarchical Disentangling Encoder, supported by the high-order dense process, and transformer to obtain densely encoded sequences with long-range correspondence. then, we present a second-order disentanglement method to improve the dense sequence's decoding process. Extensive experiments and model discussions illustrate DTEC-Net's effectiveness, which outperforms the previous state-of-the-art methods on a benchmark dataset, and greatly reduces metal artifacts while restoring richer texture details.

关键词： Metal artifact reduction CT image restoration Unsupervised learning Enhanced coding

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MedNeXt: Transformer-Driven Scaling of ConvNets for medical image Segmentation 26th

MedNeXt: Transformer-Driven Scaling of ConvNets for Medical ...

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

作者： Roy, Saikat Koehler, Gregor Ulrich, Constantin Baumgartner, Michael Petersen, Jens Isensee, Fabian Jaeger, Paul F. Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp MIC Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany NCT Heidelberg Natl Ctr Tumor Dis NCT Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany

ISBN: (纸本)9783031439001;9783031439018

there has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and consequently, are easily trainable to high performance. Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet by mirroring Transformer blocks. In this work, we improve upon this to design a modernized and scalable convolutional architecture customized to challenges of data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired large kernel segmentation network which introduces - 1) A fully ConvNeXt 3D Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up and downsampling blocks to preserve semantic richness across scales, 3) A novel technique to iteratively increase kernel sizes by upsampling small kernel networks, to prevent performance saturation on limited medical data, 4) Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt. this leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation. Our code is made publicly available at: https://***/MIC-DKFZ/MedNeXt.

关键词： medical image Segmentation Transformers MedNeXt Large Kernels ConvNeXt

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Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in medical image Segmentation 26th

Learning Transferable Object-Centric Diffeomorphic Transform...

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

作者： Kumar, Nilesh Gyawali, Prashnna K. Ghimire, Sandesh Wang, Linwei Rochester Inst Technol Rochester NY 14623 USA West Virginia Univ Morgantown WV 26506 USA Qualcomm Inc San Diego CA USA

ISBN: (纸本)9783031438943;9783031438950

Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of interest with deformable transformations can help mitigate this challenge. However, these transformations have been learned globally for the image, limiting their transferability across datasets or applicability in problems where image alignment is difficult. While object-centric augmentations provide a great opportunity to overcome these issues, existing works are only focused on position and random transformations without considering shape variations of the objects. To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image. We demonstrated its effectiveness in improving kidney tumour segmentation when leveraging shape variations learned both from within the same dataset and transferred from external datasets.

关键词： Data Augmentation Diffeomorphic transformations image Segmentation

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Text-Guided Cross-Position Attention for Segmentation: Case of medical image 26th

Text-Guided Cross-Position Attention for Segmentation: Case ...

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

作者： Lee, Go-Eun Kim, Seon Ho Cho, Jungchan Choi, Sang Tae Choi, Sang-Il Dankook Univ Yongin Gyeonggi Do South Korea Univ Southern Calif Los Angeles CA 90007 USA Gachon Univ Seongnam Gyeonggi Do South Korea Chung Ang Univ Coll Med Seoul South Korea

ISBN: (纸本)9783031439032;9783031439049

We propose a novel text-guided cross-position attention module which aims at applying a multi-modality of text and image to position attention in medical image segmentation. To match the dimension of the text feature to that of the image feature map, we multiply learnable parameters by text features and combine the multi-modal semantics via cross-attention. It allows a model to learn the dependency between various characteristics of text and image. Our proposed model demonstrates superior performance compared to other medical models using image-only data or image-text data. Furthermore, we utilize our module as a region of interest (RoI) generator to classify the inflammation of the sacroiliac joints. the RoIs obtained from the model contribute to improve the performance of classification models.

关键词： image Segmentation Multi Modal Learning Cross Position Attention Text-Guided Attention medical image

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Pre-trained Diffusion Models for Plug-and-Play medical image Enhancement 1

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

作者： Ma, Jun Zhu, Yuanzhi You, Chenyu Wang, Bo Univ Hlth Network Peter Munk Cardiac Ctr Toronto ON Canada Univ Toronto Dept Lab Med & Pathobiol Toronto ON Canada Vector Inst Artificial Intelligence Toronto ON Canada Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Yale Univ Dept Elect Engn New Haven CT USA Univ Toronto Dept Comp Sci Toronto ON Canada Univ Hlth Network AI Hub Toronto ON Canada

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Deep learning-based medical image enhancement methods (e.g., denoising and super-resolution) mainly rely on paired data and correspondingly the well-trained models can only handle one type of task. In this paper, we address the limitation with a diffusion model-based framework that mitigates the requirement of paired data and can simultaneously handle multiple enhancement tasks by one pre-trained diffusion model without fine-tuning. Experiments on low-dose CT and heart MR datasets demonstrate that the proposed method is versatile and robust for image denoising and super-resolution. We believe our work constitutes a practical and versatile solution to scalable and generalizable image enhancement.

关键词： image enhancement

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M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision 26th

M-GenSeg: Domain Adaptation for Target Modality Tumor Segmen...

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

作者： Alefsen, Malo Vorontsov, Eugene Kadoury, Samuel Ecole Polytech Montreal Montreal PQ Canada CHUM Ctr Rech Montreal PQ Canada Paige Montreal PQ Canada

ISBN: (纸本)9783031439001;9783031439018

Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different imaging modalities. this shortcoming, amplified by the limited availability of expert annotated data, has been hampering the deployment of such methods at a larger scale across modalities. To address these issues, we propose M-GenSeg, a new semi-supervised generative training strategy for cross-modality tumor segmentation on unpaired bi-modal datasets. With the addition of known healthy images, an unsupervised objective encourages the model to disentangling tumors from the background, which parallels the segmentation task. then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality. We evaluated the performance on a brain tumor segmentation dataset composed of four different contrast sequences from the public BraTS 2020 challenge data. We report consistent improvement in Dice scores over state-of-the-art domain-adaptive baselines on the unannotated target modality. Unlike the prior art, M-GenSeg also introduces the ability to train with a partially annotated source modality.

关键词： image Segmentation Semi-supervised Learning Unpaired image-to-image Translation

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MDViT: Multi-domain Vision Transformer for Small medical image Segmentation Datasets 26th

MDViT: Multi-domain Vision Transformer for Small Medical Ima...

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

作者： Du, Siyi Bayasi, Nourhan Hamarneh, Ghassan Garbi, Rafeef Univ British Columbia Vancouver BC Canada Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9783031439001;9783031439018

Despite its clinical utility, medical image segmentation (MIS) remains a daunting task due to images' inherent complexity and variability. Vision transformers (ViTs) have recently emerged as a promising solution to improve MIS;however, they require larger training datasets than convolutional neural networks. To overcome this obstacle, data-efficient ViTs were proposed, but they are typically trained using a single source of data, which overlooks the valuable knowledge that could be leveraged from other available datasets. Naivly combining datasets from different domains can result in negative knowledge transfer (NKT), i.e., a decrease in model performance on some domains with non-negligible inter-domain heterogeneity. In this paper, we propose MDViT, the first multi-domain ViT that includes domain adapters to mitigate data-hunger and combat NKT by adaptively exploiting knowledge in multiple small data resources (domains). Further, to enhance representation learning across domains, we integrate a mutual knowledge distillation paradigm that transfers knowledge between a universal network (spanning all the domains) and auxiliary domain-specific network branches. Experiments on 4 skin lesion segmentation datasets show that MDViT outperforms state-of-the-art algorithms, with superior segmentation performance and a fixed model size, at inference time, even as more domains are added. Our code is available at https://***/siyi-wind/MDViT.

关键词： Vision Transformer Data-efficiency Multi-domain Learning medical image Segmentation Dermatology

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

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

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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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Certification of Deep Learning Models for medical image Segmentation 26th

Certification of Deep Learning Models for Medical Image Segm...

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

作者： Laousy, Othmane Araujo, Alexandre Chassagnon, Guillaume Paragios, Nikos Revel, Marie-Pierre Vakalopoulou, Maria Univ Paris Saclay Cent Supelec MICS Gif Sur Yvette France Paris Cite Univ Hop Cochin AP HP Paris France Inria Saclay Gif Sur Yvette France NYU New York NY USA Therapanacea Paris France

ISBN: (纸本)9783031439001;9783031439018

In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification models, segmentation models are affected by adversarial attacks. In a safety-critical field like healthcare, certifying model predictions is of the utmost importance. Randomized smoothing has been introduced lately and provides a framework to certify models and obtain theoretical guarantees. In this paper, we present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models. Our results show that leveraging the power of denoising diffusion probabilistic models helps us overcome the limits of randomized smoothing. We conduct extensive experiments on five public datasets of chest X-rays, skin lesions, and colonoscopies, and empirically show that we are able to maintain high certified Dice scores even for highly perturbed images. Our work represents the first attempt to certify medical image segmentation models, and we aspire for it to set a foundation for future benchmarks in this crucial and largely uncharted area.

关键词： Certified Robustness Randomized Smoothing Denoising Diffusion Models Segmentation

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HartleyMHA: Self-attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D image Segmentation 26th

HartleyMHA: Self-attention in Frequency Domain for Resolutio...

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

作者： Wong, Ken C. L. Wang, Hongzhi Syeda-Mahmood, Tanveer IBM Res Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)9783031439001;9783031439018

With the introduction of Transformers, different attention-based models have been proposed for image segmentation with promising results. Although self-attention allows capturing of long-range dependencies, it suffers from a quadratic complexity in the image size especially in 3D. To avoid the out-of-memory error during training, input size reduction is usually required for 3D segmentation, but the accuracy can be suboptimal when the trained models are applied on the original image size. To address this limitation, inspired by the Fourier neural operator (FNO), we introduce the HartleyMHA model which is robust to training image resolution with efficient self-attention. FNO is a deep learning framework for learning mappings between functions in partial differential equations, which has the appealing properties of zero-shot super-resolution and global receptive field. We modify the FNO by using the Hartley transform with shared parameters to reduce the model size by orders of magnitude, and this allows us to further apply self-attention in the frequency domain for more expressive high-order feature combination with improved efficiency. When tested on the BraTS'19 dataset, it achieved superior robustness to training image resolution than other tested models with less than 1% of their model parameters.

关键词： image segmentation Transformer Fourier neural operator Hartley transform Resolution-robust

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