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A Hybrid Propagation Network for Interactive Volumetric image Segmentation 1

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Shi, Luyue Zhang, Xuanye Liu, Yunbi Han, Xiaoguang Shenzhen Res Inst Big Data Shenzhen Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

ISBN: (数字)9783031164408

ISBN: (纸本)9783031164408;9783031164392

Interactive segmentation is of great importance in clinical practice for correcting and refining the automated segmentation by involving additional user hints, e.g., scribbles and clicks. Currently, interactive segmentation methods for 2D medical images are well studied, while seldom works are conducted on 3D medical volumetric data. Given a 3D volumetric image, the user interaction can only be performed on a few slices, thus the key issue is how to propagate the information over the entire volume for spatial-consistent segmentation. In this paper, we propose a novel hybrid propagation network for interactive segmentation of 3D medical images. Our proposed method consists of two key designs, including a slice propagation network (denoted as SPN) for transferring user hints to adjacent slices to guide the segmentation slice-by-slice and a volume propagation network (denoted as VPN) for propagating user hints over the entire volume in a global manner. Specifically, as for SPN, we adopt a memory-augmented network, which utilizes the information of segmented slices (memory slices) to propagate interaction information. To use interaction information propagated by VPN, a feature-enhanced memory module is designed, in which the volume segmentation information from the latent space of VPN is introduced into the memory module of SPN. In such a way, the interactive segmentation can leverage both advantages of volume and slice propagation, thus improving the volume segmentation results. We perform experiments on two commonly-used 3D medical datasets, with the experimental results indicating that our method outperforms the state-of-the-art methods. Our code is available at https://***/luyueshi/Hybrid-Propagation.

关键词： Interactive segmentation Hybrid propagation 3D medical images

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Semi-supervised medical image Classification with Temporal Knowledge-Aware Regularization 25th

Semi-supervised Medical Image Classification with Temporal K...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Yang, Qiushi Liu, Xinyu Chen, Zhen Ibragimov, Bulat Yuan, Yixuan City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Univ Copenhagen Dept Comp Sci DK-2100 Copenhagen Denmark

ISBN: (纸本)9783031164521;9783031164514

Semi-supervised learning (SSL) for medical image classification has achieved exceptional success on efficiently exploiting knowledge from unlabeled data with limited labeled data. Nevertheless, recent SSL methods suffer from misleading hard-form pseudo labeling, exacerbating the confirmation bias issue due to rough training process. Moreover, the training schemes excessively depend on the quality of generated pseudo labels, which is vulnerable against the inferior ones. In this paper, we propose TEmporal knowledge-Aware Regularization (TEAR) for semisupervised medical image classification. Instead of using hard pseudo labels to train models roughly, we design Adaptive Pseudo Labeling (AdaPL), a mild learning strategy that relaxes hard pseudo labels to soft-form ones and provides a cautious training. AdaPL is built on a novel theoretically derived loss estimator, which approximates the loss of unlabeled samples according to the temporal information across training iterations, to adaptively relax pseudo labels. To release the excessive dependency of biased pseudo labels, we take advantage of the temporal knowledge and propose Iterative Prototype Harmonizing (IPH) to encourage the model to learn discriminative representations in an unsupervised manner. the core principle of IPH is to maintain the harmonization of clustered prototypes across different iterations. Both AdaPL and IPH can be easily incorporated into prior pseudo labeling-based models to extract features from unlabeled medical data for accurate classification. Extensive experiments on three semi-supervised medical image datasets demonstrate that our method outperforms state-of-theart approaches. the code is available at littps://***/City U-AIM-Group/TEAR.

关键词： Semi-supervised learning medical image classification Temporal knowledge Pseudo labeling Prototype harmonizing

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Heterogeneous Face Recognition Algorithm: Convolution Neural Network Approach 5

Heterogeneous Face Recognition Algorithm: Convolution Neural...

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5th international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sujatha, E. Manickam, M. Ani Minisha, R. Rekha, K.S. Indumathy, T. Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rr and Dr Sr Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India Department of Medical Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350385649

Heterogeneous face recognition, also known as HFR, is an approach to facial recognition that attempts to match facial pictures that have been gathered through a variety of sensory modalities. HFR is primarily used for forensics, network security, and all commercial applications. the heterogeneous face recognition (HFR) algorithm is robust in handling challenges that exist in conventional face recognition. this is since heterogeneous face images display an enormous amount of intra- class variation, and it does not support hybrid modality of face images. this study provides a unique technique to transfer learning as a means of mastering invariant facial feature characteristics in visual (VIS) face pictures. A wrapping procedure combines end to end connections of two components for deep network. A discriminator has the details of fine-grained data for improving the quality of image which depends on wavelets. the system is made up of two different modules: one deals with face recognition, while the other determines a person's gender. It analysis about the characteristics of gender from the image using Convolutional Neural Networks that have been pre-trained. To train CNN in the face recognition module, in particular, the use of the open-source datasets Labelled Faces within the Wild, YouTube Face, and VGGFace2 results in an improvement in accuracy. Deep learning techniques and a substantial number of photographic examples of people's faces are what make it feasible for the proposed recognition to function so admirably. the problem of studying recognition remains challenging due to the limited size of the sample and the information gap between the senses. © 2024 IEEE.

关键词： Face recognition

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Poisson2Sparse: Self-supervised Poisson Denoising from a Single image 25th

Poisson2Sparse: Self-supervised Poisson Denoising from a Sin...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Ta, Calvin-Khang Aich, Abhishek Gupta, Akash Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9783031164521;9783031164514

image enhancement approaches often assume that the noise is signal independent, and approximate the degradation model as zero-mean additive Gaussian. However, this assumption does not hold for biomedical imaging systems where sensor-based sources of noise are proportional to signal strengths, and the noise is better represented as a Poisson process. In this work, we explore a sparsity and dictionary learning-based approach and present a novel self-supervised learning method for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data. Specifically, we approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network that enforces sparsity with respect to the weights of the network. Since the sparse representations are based on the underlying image, it is able to suppress the spurious components (noise) in the image patches, thereby introducing implicit regularization for denoising tasks through the network structure. Experiments on two bio-imaging datasets demonstrate that our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM. Our qualitative results demonstrate that, in addition to higher performance on standard quantitative metrics, we are able to recover much more subtle details than other compared approaches. Our code is made publicly available at https://***/tacalvin/Poisson2Sparse.

关键词： image enhancement

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Swin-MMC: Swin-Based Model for Myopic Maculopathy Classification in Fundus images 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

作者： Lu, Li Pan, Xuhao Jin, Panji Ding, Ye School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China School of Statistics and Information Shanghai University of International Business and Economics Shanghai201620 China

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic maculopathy is a highly myopic retinal disorder that often occurs in highly myopic patients, serving as a major cause of visual impairment and blindness in numerous countries. Currently, fundus images serve as a prevalent diagnostic tool for myopic maculopathy. However, its efficacy relies on the expertise of clinicians, making the process labor-intensive. thus, we propose a model specifically designed for the image classification of myopic maculopathy, named Swin-MMC, based on the Swin Transformer model architecture, which achieves outstanding performance on the test dataset. To achieve a finer-grained classification of myopic maculopathy in fundus images, we have innovatively and for the first time proposed the use of enhanced ArcFace loss in medical image classification. then, based on the Swin-MMC model, we introduce a weak label strategy that effectively mitigates overfitting. Our approach achieves significantly improved results on the test dataset and can be easily used for various datasets and classification tasks. We conduct a series of experiments in the MMAC2023 challenge. In the testing phase, our average performance metric reaches 86.60%. In the further testing phase, our model’s performance improves to 88.23%, ultimately securing the championship in the MMAC2023 challenge. the codes allowing replication of this study have been made publicly available at https://***/LuliDreamAI/MICCAI_TASK1. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework with Cycle-Consistent Constraints 25th

Mapping in Cycles: Dual-Domain PET-CT Synthesis Framework wi...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zhang, Jiadong Cui, Zhiming Jiang, Caiwen Zhang, Jingyang Gao, Fei Shen, Dinggang ShanghaiTech Univ Sch Biomed Engn Shanghai 201210 Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai 200230 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Positron emission tomography (PET) is an important medical imaging technique, especially for brain and cancer disease diagnosis. Modern PET scanner is usually combined with computed tomography (CT), where CT image is used for anatomical localization, PET attenuation correction, and radiotherapy treatment planning Considering radiation dose of CT image as well as increasing spatial resolution of PET image, there is a growing demand to synthesize CT image from PET image (without scanning CT) to reduce risk of radiation exposure. However, most existing works perform learning-based image synthesis to construct cross-modality mapping only in the image domain, without considering of the projection domain, leading to potential physical inconsistency. To address this problem, we propose a novel PET-CT synthesis framework by exploiting dual-domain information (i.e., image domain and projection domain). Specifically, we design both image domain network and projection domain network to jointly learn high-dimensional mapping from PET to CT. the image domain and the projection domain can be connected together with a forward projection (FP) and a filtered back projection (FBP). To further help the PET-to-CT synthesis task, we also design a secondary CT-toPET synthesis task with the same network structure, and combine the two tasks into a bidirectional mapping framework with several closed cycles. More importantly, these cycles can serve as cycle-consistent losses to further help network training for better synthesis performance. Extensive validations on the clinical PET-CT data demonstrate the proposed PET-CT synthesis framework outperforms the state-of-the-art (SOTA) medical image synthesis methods with significant improvements.

关键词： medical image synthesis Dual-domain information Cycle-consistent constraint

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End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation 25th

End-to-End Multi-Slice-to-Volume Concurrent Registration and...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Leroy, Amaury Lerousseau, Marvin Henry, theophraste Cafaro, Alexandre Paragios, Nikos Gregoire, Vincent Deutsch, Eric Therapanacea Paris France Paris Saclay Univ Cent Supelec Gif Sur Yvette France Ctr Leon Berard Dept Radiat Oncol Lyon France Paris Saclay Univ Gustave Roussy Inserm Villejuif France

ISBN: (纸本)9783031164460;9783031164453

For interventional procedures, a real-time mapping between treatment guidance images and planning data is challenging yet essential for successful therapy implementation. Because of time and machine constraints, it involves imaging of different modalities, resolutions and dimensions, along with severe out-of-plane deformations to handle. In this paper, we introduce MSV-RegSyn-Net, a novel, scalable, deep learning-based framework for concurrent slice-to-volume registration and high-resolution modality transfer synthesis. It consists of an end-to-end pipeline made up of (i) a cycle generative adversarial network for multimodal image translation combined with (ii) a multi-slice-to-volume deformable registration network. the concurrent nature of our approach creates mutual benefit for both tasks: image translation is naturally eased by explicit handling of out-of-plane deformations while registration benefits from bringing multimodal signals into the same domain. Our model is fully unsupervised and does not require any ground-truth deformation or segmentation mask. It obtains superior qualitative and quantitative performance for multi-slice MR to 3D CT pelvic imaging compared to state-of-the-art traditional and learning-based methods on both tasks.

关键词： Multimodal registration 2D-3D image-to-image translation Generative adversarial network Unsupervised learning

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Domain Specific Convolution and High Frequency Reconstruction Based Unsupervised Domain Adaptation for medical image Segmentation 25th

Domain Specific Convolution and High Frequency Reconstructio...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Hu, Shishuai Liao, Zehui Xia, Yong Northwestern Polytech Univ Natl Engn Lab Integrated Aerosp Ground Ocean Big Sch Comp Sci & Engn Xian 710072 Peoples R China Northwestern Polytech Univ Ningbo Inst Ningbo 315048 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China

ISBN: (纸本)9783031164491;9783031164484

Although deep learning models have achieved remarkable success in medical image segmentation, the domain shift issue caused mainly by the highly variable quality of medical images is a major hurdle that prevents these models from being deployed for real clinical practices, since no one can predict the performance of a 'well-trained' model on a set of unseen clinical data. Previously, many methods have been proposed based on, for instance, CycleGAN or the Fourier transform to address this issue, which, however, suffer from either an inadequate ability to preserve anatomical structures or unexpectedly introduced artifacts. In this paper, we propose a multi-source-domain unsupervised domain adaptation (UDA) method called Domain specific Convolution and high frequency Reconstruction (DoCR) for medical image segmentation. We design an auxiliary high frequency reconstruction (HFR) task to facilitate UDA, and hence avoid the interference of the artifacts generated by the low-frequency component replacement. We also construct the domain specific convolution (DSC) module to boost the segmentation model's ability to domain-invariant features extraction. We evaluate DoCR on a benchmark fundus image dataset. Our results indicate that the proposed DoCR achieves superior performance over other UDA methods in multi-domain joint optic cup and optic disc segmentation. Code is available at: https://***/ShishuaiHu/DoCR.

关键词： Unsupervised domain adaptation medical image segmentation Domain specific convolution High frequency reconstruction

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Domain Unlearning Boosts Lesion Segmentation Performance on Seen and Unseen MR Scanner Data 9th

Domain Unlearning Boosts Lesion Segmentation Performance on ...

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9th international Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th medical image computing and computer assisted intervention, MICCAI 2023

作者： Preložnik, Domen Špiclin, Žiga Faculty of Electrical Engineering University of Ljubljana Tržaška Cesta 24 Ljubljana1000 Slovenia

ISBN: (纸本)9783031761591

Inter-scanner variability was highlighted in the 2020 MAGNIMS consensus guidelines as most-detrimental factor in image acquisition with high impact on the diagnostic and prognostic quality of the MR scans. this study aimed to evaluate and compare the contributions of domain unlearning on white matter lesion segmentation from the MR scans. We used the MSSEG 2016 challenge dataset of 53 MS patients, where MR images were acquired on 4 different scanners, one of which was absent in the training split. Our approach was the state-of-the-art Swin UNETR segmentation model that was adopted for domain unlearning to better handle inter-scanner bias. Performance of lesion segmentation was evaluated according to challenge protocol and compared to three best challenge results and the state-of-the-art results of recently proposed Dense Residual UNET method. the baseline Swin UNETR model achieved comparable results to the three challenge methods, while our domain unlearning model consistently improved in all metrics versus the baseline model and achieved the highest Dice score among all tested methods. the principal impact of unlearning was in reducing false positive annotations, whereas performance consistently improved on seen and unseen scanner data, indicating that scanner-specific intensity artifacts do not overlap with the information required for lesion segmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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Unsupervised Domain Adaptive Fundus image Segmentation with Category-Level Regularization 25th

Unsupervised Domain Adaptive Fundus Image Segmentation with ...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Feng, Wei Wang, Lin Ju, Lie Zhao, Xin Wang, Xin Shi, Xiaoyu Ge, Zongyuan Monash Univ Monash Med AI Grp Melbourne Australia Monash Univ Airdoc Monash Res Ctr Clayton Vic Australia Monash Univ Monash eResearch Ctr Clayton Vic Australia Airdoc LLC Beijing Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks. However, these methods focus only on global distribution adaptation and ignore distribution constraints at the category level, which would lead to sub-optimal adaptation performance. this paper presents an unsupervised domain adaptation framework based on category-level regularization that regularizes the category distribution from three perspectives. Specifically, for inter-domain category regularization, an adaptive prototype alignment module is proposed to align feature prototypes of the same category in the source and target domains. In addition, for intra-domain category regularization, we tailored a regularization technique for the source and target domains, respectively. In the source domain, a prototype-guided discriminative loss is proposed to learn more discriminative feature representations by enforcing intra-class compactness and inter-class separability, and as a complement to traditional supervised loss. In the target domain, an augmented consistency category regularization loss is proposed to force the model to produce consistent predictions for augmented/unaugmented target images, which encourages semantically similar regions to be given the same label. Extensive experiments on two publicly fundus datasets show that the proposed approach significantly outperforms other state-of-theart comparison algorithms' (Our code is available at https://***/ fengweie/UDA_CLR.).

关键词： Unsupervised domain adaptation Category level regularization Fundus image segmentation

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