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Reliability-Aware Contrastive Self-ensembling for Semi-supervised medical image Classification 25th

Reliability-Aware Contrastive Self-ensembling for Semi-super...

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

作者： Hang, Wenlong Huang, Yecheng Liang, Shuang Lei, Baiying Choi, Kup-Sze Qin, Jing Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommunicat Smart Hlth Big Data Anal & Locat Serv Engn Lab Ji Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China Hong Kong Polytech Univ Sch Nursing Hung Hom Hong Kong Peoples R China

ISBN: (纸本)9783031164316;9783031164309

Self-ensembling framework has proven to be a powerful paradigm for semi-supervised medical image classification by leveraging abundant unlabeled data. However, the unlabeled data used in most of self-ensembling methods are equally weighted, which adversely affects the classification performance of models when difference exists among unlabeled data acquired from different populations, equipment and environments. To address this issue, we propose a novel reliability-aware contrastive self-ensembling framework, which can leverage the reliable unlabeled data selectively. Concretely, we introduce a weight function to the mean teacher paradigm for mapping the probability predictions of unlabeled data to corresponding weights that reflect their reliability. Hence, we can safely leverage the predictions of related unlabeled data under different perturbations to construct a reliable consistency loss. Besides, we further design a novel reliable contrastive loss to achieve better intra-class compactness and inter-class separability for the normalized embeddings derived from related unlabeled data. As a result, our reliability-aware scheme enables the contrastive self-ensembling framework concurrently capture both the reliable data-level and data-structure-level information, thereby improving the robustness and generalization power of the model. Experiments on two publicly available medical image datasets demonstrate the superiority of the proposed method. Our model is available at https://***/Mwnic/RAC-MT.

关键词： Self-ensembling Weight function Reliability medical image classification

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DISARM: Disentangled Scanner-Free image Generation via Unsupervised image2image Translation 7th

DISARM: Disentangled Scanner-Free Image Generation via Unsu...

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7th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Caldera, Luca Cavinato, Lara Cappozzo, Andrea Cama, Isabella Garbarino, Sara Cirone, Alessio Lodi, Raffaele Tagliavini, Fabrizio Nigri, Anna De Francesco, Silvia Ieva, Francesca MOX Department of Mathematics Politecnico di Milano Milan Italy Department of Mathematics Università di Genova Genova Italy IRCCS Ospedale Policlinico San Martino Genova Italy Department of Biomedical and Neuromotor Sciences University of Bologna Bologna Italy and Neuropathology IRCCS Istituto Neurologico Carlo Besta Milan Italy Neuroradiology Unit IRCCS Istituto Neurologico Carlo Besta Milan Italy Laboratory of Neuroinformatics IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy

ISBN: (纸本)9783031787607

Ensuring reproducibility of Magnetic Resonance (MR) images from different scanners is crucial in multicenter studies, as scanner-induced variability is known to impact the results significantly. To address this problem, we introduce a novel unsupervised deep learning approach aimed at achieving 3 primary objectives/advantages: (1) create a scanner-free space that enables the uniform transfer of images across different scanners in a denoised setting, (2) impart the appearance of a specific training scanner to images, transferring its unique characteristics, (3) avoid time-consuming preprocessing of MR images. the proposed methodology is based on disentangling image information into two distinct spaces: one encoding the scanner-specific information and one capturing the anatomical/biological structure of the image. We trained our model on two open-source datasets (ADNI and PPMI) of T1-weighted brain MR images of normal control patients. We tested it on a real-world clinical dataset from the Italian Neuroimaging Network, comparing its performance with a state-of-the-art model. the results show the superiority of the proposed model in harmonizing images for clinical research, demonstrating its effectiveness in achieving consistent and reproducible harmonization of the MR images across (unseen) scanning environments. Code is available at https://***/luca2245/DISARM_Harmonization. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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Multi-depth Boundary-Aware Left Atrial Scar Segmentation Network 1

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1st Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, held in Conjunction with the 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

作者： Wu, Mengjun Ding, Wangbin Yang, Mingjing Huang, Liqin College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9783031317781

ISBN: (纸本)9783031317774

Automatic segmentation of left atrial (LA) scars from late gadolinium enhanced CMR images is a crucial step for atrial fibrillation (AF) recurrence analysis. However, delineating LA scars is tedious and error-prone due to the variation of scar shapes. In this work, we propose a boundary-aware LA scar segmentation network, which is composed of two branches to segment LA and LA scars, respectively. We explore the inherent spatial relationship between LA and LA scars. By introducing a Sobel fusion module between the two segmentation branches, the spatial information of LA boundaries can be propagated from the LA branch to the scar branch. thus, LA scar segmentation can be performed condition on the LA boundaries regions. In our experiments, 40 labeled images were used to train the proposed network, and the remaining 20 labeled images were used for evaluation. the network achieved an average Dice score of 0.608 for LA scar segmentation. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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Personalized Diagnostic Tool for thyroid Cancer Classification Using Multi-view Ultrasound 1

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

作者： Huang, Han Dong, Yijie Jia, Xiaohong Zhou, Jianqiao Ni, Dong Cheng, Jun Huang, Ruobing Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr NationalReg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Shanghai Jiao Tong Univ Sch Med Ruijin Hosp Dept Ultrasound Med Shanghai Peoples R China

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from both transverse and longitudinal views using thyroid ultrasound. However, the appearance of the thyroid gland and lesions can vary dramatically across individuals. Identifying key diagnostic information from both views requires specialized expertise. Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis. To address these, we propose a personalized diagnostic tool that can customize its decision-making process for different patients. It consists of a multi-view classification module for feature extraction and a personalized weighting allocation network that generates optimal weighting for different views. It is also equipped with a self-supervised view-aware contrastive loss to further improve the model robustness towards different patient groups. Experimental results show that the proposed framework can better utilize multi-view information and outperform the competing methods.

关键词： Personalized diagnosis thyroid cancer Ultrasound Multi-view

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SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints

SegHeD: Segmentation of Heterogeneous Data for Multiple Sc...

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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： Basaran, Berke Doga Zhang, Xinru Matthews, Paul M. Bai, Wenjia Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom School of Integrated Circuits and Electronics Beijing Institute of Technology CN Beijing China UK Dementia Research Institute Imperial College London London United Kingdom Rosalind Franklin Institute Didcot United Kingdom

ISBN: (纸本)9783031845246

Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS). Machine learning models have demonstrated a great potential for automated MS lesion segmentation. Training such models typically requires large-scale high-quality datasets that are consistently annotated. However, MS imaging datasets are often small, segregated across multiple sites, with different formats (cross-sectional or longitudinal), and diverse annotation styles. this poses a significant challenge to train a unified MS lesion segmentation model. To tackle this challenge, we present SegHeD, a novel multi-dataset multi-task segmentation model that can incorporate heterogeneous data as input and perform all-lesion, new-lesion, as well as vanishing-lesion segmentation. Furthermore, we account for domain knowledge about MS lesions, incorporating longitudinal, spatial, and volumetric constraints into the segmentation model. SegHeD is assessed on five MS datasets and achieves a high performance in all, new, and vanishing-lesion segmentation, outperforming several state-of-the-art methods in this field. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Weakly Supervised MR-TRUS image Synthesis for Brachytherapy of Prostate Cancer 25th

Weakly Supervised MR-TRUS Image Synthesis for Brachytherapy ...

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

作者： Pang, Yunkui Chen, Xu Huang, Yunzhi Yap, Pew-thian Lian, Jun Univ N Carolina Chapel Hill NC 27599 USA Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Prostate magnetic resonance imaging (MRI) offers accurate details of structures and tumors for prostate cancer brachytherapy. However, it is unsuitable for routine treatment since MR images differ significantly from trans-rectal ultrasound (TRUS) images conventionally used for radioactive seed implants in brachytherapy. TRUS imaging is fast, convenient, and widely available in the operation room but is known for its low soft-tissue contrast and tumor visualization capability in the prostate area. Conventionally, practitioners usually rely on prostate segmentation to fuse the two imaging modalities with non-rigid registration. However, prostate delineation is often not available on diagnostic MR images. Besides, the high non-linear intensity relationship between two imaging modalities poses a challenge to non-rigid registration. Hence, we propose a method to generate a TRUS-styled image from a prostate MR image to replace the role of the TRUS image in radiation therapy dose pre-planning. We propose a structural constraint to handle non-linear projections of anatomical structures between MR and TRUS images. We further include an adversarial mechanism to enforce the model to preserve anatomical features in an MR image (such as prostate boundary and dominant intraprostatic lesion (DIL)) while synthesizing the TRUS-styled counterpart image. the proposed method is compared with other state-of-art methods with real TRUS images as the reference. the results demonstrate that the TRUS images synthesized by our method can be used for brachytherapy treatment planning for prostate cancer.

关键词： MR TRUS Synthesize Weakly supervised

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HEMIT: H&E to Multiplex-Immunohistochemistry image Translation with Dual-Branch Pix2pix Generator

HEMIT: H&E to Multiplex-Immunohistochemistry Image Translat...

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作者： Bian, Chang Phillips, Beth Cootes, Tim Fergie, Martin Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester Manchester United Kingdom Division of Cancer Sciences University of Manchester Manchester United Kingdom

ISBN: (纸本)9783031845246

Computational analysis of multiplexed immunofluorescence histology data is emerging as an important method for understanding the tumour micro-environment in cancer. this work presents HEMIT, a dataset designed for translating Hematoxylin and Eosin (H&E) sections to multiplex-immunohistochemistry (mIHC) images, simultaneously featuring DAPI, CD3, and panCK markers. Distinctively, HEMIT’s mIHC images are multi-component and cellular-level aligned with H&E, enriching supervised stain translation tasks. To our knowledge, HEMIT is the first publicly available cellular-level aligned dataset that enables H&E to multi-target mIHC image translation. this dataset provides the computer vision community with a valuable resource to develop novel computational methods which have the potential to gain new insights from H&E slide archives. We also propose a new dual-branch generator architecture combining residual Convolutional Neural Networks (CNNs) and Swin Transformers, with a feature map fusion module to integrate information from both branches. this architecture achieves superior translation outcomes compared to other popular algorithms. Evaluations on the HEMIT dataset show it outperforms pix2pixHD, pix2pix, U-Net, and ResNet, with the highest scores in Structural Similarity Index Measure (SSIM), Pearson correlation (R), and Peak Signal-to-Noise Ratio (PSNR). Additionally, we discuss the limitations of commonly used metrics in certain stain translation scenarios and provide recommendations for future use. Additionally, we also designed a downstream analysis to further validate the quality and utility of the generated mIHC images from a clinical-focused perspective. these results set a new benchmark in the field of stain translation tasks. the proposed dataset can be accessed at: https://***/BianChang/HEMIT-DATASET. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Multi-scale Super-Resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness 25th

Multi-scale Super-Resolution Magnetic Resonance Spectroscopi...

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

作者： Dong, Siyuan Hangel, Gilbert Bogner, Wolfgang Widhalm, Georg Roessler, Karl Trattnig, Siegfried You, Chenyu de Graaf, Robin Onofrey, John A. Duncan, James S. Yale Univ Elect Engn New Haven CT 06520 USA Med Univ Vienna Biomed Imaging & Image Guided Therapy Highfield MR Ctr Vienna Austria Med Univ Vienna Neurosurg Vienna Austria Yale Univ Radiol & Biomed Imaging New Haven CT USA

ISBN: (纸本)9783031164460;9783031164453

Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions. the existing deep learning-based MRSI super-resolution methods require training a separate network for each upscaling factor, which is time-consuming and memory inefficient. We tackle this multi-scale super-resolution problem using a Filter Scaling strategy that modulates the convolution filters based on the upscaling factor, such that a single network can be used for various upscaling factors. Observing that each metabolite has distinct spatial characteristics, we also modulate the network based on the specific metabolite. Furthermore, our network is conditioned on the weight of adversarial loss so that the perceptual sharpness of the super-resolved metabolic maps can be adjusted within a single network. We incorporate these network conditionings using a novel MultiConditional Module. the experiments were carried out on a H-1-MRSI dataset from 15 high-grade glioma patients. Results indicate that the proposed network achieves the best performance among several multiscale super-resolution methods and can provide super-resolved metabolic maps with adjustable sharpness. Our code is available at https://github. com/dsy199610/Multiscale-SR-MRSI-adjustable-sharpness.

关键词： Brain MRSI Super-resolution Network conditioning

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Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT 25th

Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modal...

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

作者： Chen, Xiongchao Zhou, Bo Xie, Huidong Guo, Xueqi Zhang, Jiazhen Sinusas, Albert J. Onofrey, John A. Liu, Chi Yale Univ Dept Biomed Engn New Haven CT 06520 USA Yale Univ Dept Radiol & Biomed Imaging New Haven CT USA Yale Univ Dept Internal Med New Haven CT USA

ISBN: (纸本)9783031164460;9783031164453

Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (mu-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated two input modalities as an early feature fusion or extracted image features using two separate CNN modules for a late fusion. these methods do not fully extract or fuse the cross-modality information. Besides, deep-learningbased rigid registration of cardiac SPECT and CT-derived mu-maps has not been investigated before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived mu-maps. DuSFE fuses the knowledge from multiple modalities to recalibrate both channel-wise and spatial features for each modality. DuSFE can be embedded at multiple convolutional layers to enable feature fusion at different spatial dimensions. Our studies using clinical data demonstrated that a network embedded with DuSFE generated substantial lower registration errors and therefore more accurate AC SPECT images than previous methods. Our source code is available at https://***/XiongchaoChen/DuSFE_CrossRegistration.

关键词： Cardiac SPECT/CT Cross-modality image registration Attenuation correction

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Diffusion Deformable Model for 4D Temporal medical image Generation 25th

Diffusion Deformable Model for 4D Temporal Medical Image Gen...

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

作者： Kim, Boah Ye, Jong Chul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9783031164316;9783031164309

Temporal volume images with 3D-ht (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural images have been extensively studied, approaches for temporal medical image generation such as 4D cardiac volume data are limited. In this work, we present a novel deep learning model that generates intermediate temporal volumes between source and target volumes. Specifically, we propose a diffusion deformable model (DDM) by adapting the denoising diffusion probabilistic model that has recently been widely investigated for realistic image generation. Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path. Once our model is trained, the latent code estimated from the diffusion module is simply interpolated and fed into the deformation module, which enables DDM to generate temporal frames along the continuous trajectory while preserving the topology of the source image. We demonstrate the proposed method with the 4D cardiac MR image generation between the diastolic and systolic phases for each subject. Compared to the existing deformation methods, our DDM achieves high performance on temporal volume generation.

关键词： Deep learning medical image generation image deformation Diffusion model

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