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NVUM: Non-volatile Unbiased Memory for Robust medical image Classification 1

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

作者： Liu, Fengbei Chen, Yuanhong Tian, Yu Liu, Yuyuan Wang, Chong Belagiannis, Vasileios Carneiro, Gustavo Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Otto Guericke Univ Magdeburg Magdeburg Germany

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Real-world large-scale medical image analysis (MIA) datasets have three challenges: 1) they contain noisy-labelled samples that affect training convergence and generalisation, 2) they usually have an imbalanced distribution of samples per class, and 3) they normally comprise a multi-label problem, where samples can have multiple diagnoses. Current approaches are commonly trained to solve a subset of those problems, but we are unaware of methods that address the three problems simultaneously. In this paper, we propose a new training module called Non-Volatile Unbiased Memory (NVUM), which non-volatility stores running average of model logits for a new regularization loss on noisy multi-label problem. We further unbias the classification prediction in NVUM update for imbalanced learning problem. We run extensive experiments to evaluate NVUM on new benchmarks proposed by this paper, where training is performed on noisy multi-label imbalanced chest X-ray (CXR) training sets, formed by Chest-Xray14 and CheXpert, and the testing is performed on the clean multi-label CXR datasets OpenI and PadChest. Our method outperforms previous state-of-the-art CXR classifiers and previous methods that can deal with noisy labels on all evaluations.

关键词： Chest X-ray classification Multi-label classification Imbalanced classification

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Enhance Multi-modal and Multi-center Whole Heart Segmentation Using Data Augmentation and Model Calibration 1st

Enhance Multi-modal and Multi-center Whole Heart Segmentati...

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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： Tran, Charlie Li, Andy Espinoza, Aaron Kamal, Sayem Samuel, Anoushka Jiang, Charles Zhuang, Jian Shi, Yiyu Xu, Xiaowei University of Florida GainesvilleFL32611 United States University of Texas at Austin AustinTX78712 United States Texas State University San MarcosTX78666 United States Columbia University New York CityNY10027 United States California Polytechnic State University San Luis ObispoCA93410 United States University of Notre Dame Notre DameIN46556 United States Guangdong Provincial People’s Hospital Guangzhou510000 China

ISBN: (纸本)9783031870088

Accurate whole heart segmentation can enhance the modeling and analysis of cardiovascular disease treatment. However, real-world imaging analysis faces challenges in generalization due to data inhomogeneity, which arises from factors such as low-quality image acquisition, variations across multiple modalities, differences in scanner vendors, and cardiac motion. In this paper, to tackle the problem of data inhomogeneity, we use data augmentation and model calibration for whole heart segmentation enhancement. We first adopted the state-of-the-art MedNeXt transformer-based architecture as the baseline. then, we enhance the baseline in model robustness through an ensemble strategy with strong data augmentation and out-of-distribution model calibration techniques. Comprehensive experiments have been conducted on the dataset from the CARE2024 Task 5 Whole Heart Segmentation (WHS++) challenge, in which 7 target structures from CT and MRI images acquired from multiple centers are considered. Results show that data augmentation and model calibration can effectively improve the segmentation performance across various modalities and centers. We highlight that our team ranks first on the validation leaderboard with average dice scores of 0.9440 (CT) and 0.8956 (MRI). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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What Can We Learn About a Generated image Corrupting Its Latent Representation? 25th

What Can We Learn About a Generated Image Corrupting Its Lat...

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

作者： Tomczak, Agnieszka Gupta, Aarushi Ilic, Slobodan Navab, Nassir Albarqouni, Shadi Tech Univ Munich Fac Informat Munich Germany Siemens AG Munich Germany Indian Inst Technol Delhi Helmholtz New Delhi India Univ Hosp Bonn Clin Diagnost & Intervent Radiol Bonn Germany Helmholz Zentrum Munich Helmholtz AI Munich Germany

ISBN: (纸本)9783031164460;9783031164453

Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. they can translate images from one imaging modality to another at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite its effectiveness in learning the underlying data distribution, it can lead to a discrepancy between input and output data. the purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck. We achieve this by corrupting the latent representation with noise and generating multiple outputs. the degree of differences between them is interpreted as the strength of the representation: the more robust the latent representation, the fewer changes in the output image the corruption causes. Our results demonstrate that our proposed method has the ability to i) predict uncertain parts of synthesized images, and ii) identify samples that may not be reliable for downstream tasks, e.g., liver segmentation task.

关键词： GANs image synthesis Uncertainty image quality

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Morphology-Aware Interactive Keypoint Estimation 1

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

作者： Kim, Jinhee Kim, Taesung Kim, Taewoo Choo, Jaegul Kim, Dong-Wook Ahn, Byungduk Song, In-Seok Kim, Yoon-Ji Korea Adv Inst Sci & Technol Daejeon South Korea Korea Univ Anam Hosp Seoul South Korea Papas Dent Clin Seoul South Korea Ulsan Univ Sch Med Asan Med Ctr Seoul South Korea

ISBN: (数字)9783031164378

ISBN: (纸本)9783031164378;9783031164361

Diagnosis based on medical images, such as X-ray images, often involves manual annotation of anatomical keypoints. However, this process involves significant human efforts and can thus be a bottleneck in the diagnostic process. To fully automate this procedure, deep-learning-based methods have been widely proposed and have achieved high performance in detecting keypoints in medical images. However, these methods still have clinical limitations: accuracy cannot be guaranteed for all cases, and it is necessary for doctors to double-check all predictions of models. In response, we propose a novel deep neural network that, given an X-ray image, automatically detects and refines the anatomical keypoints through a user-interactive system in which doctors can fix mispredicted keypoints with fewer clicks than needed during manual revision. Using our own collected data and the publicly available AASCE dataset, we demonstrate the effectiveness of the proposed method in reducing the annotation costs via extensive quantitative and qualitative results.

关键词： Interactive keypoint estimation AI-assisted image analysis

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PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound images 26th

PLD-AL: Pseudo-label Divergence-Based Active Learning in Car...

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

作者： Tang, Yucheng Hu, Yipeng Li, Jing Lin, Hu Xu, Xiang Huang, Ke Lin, Hongxiang Zhejiang Univ Sch Math Sci Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China UCL Ctr Med Image Comp & Wellcome EPSRC Ctr Intervent & Surg Sci London England Zhejiang Univ Childrens Hosp Dept Endocrinol Sch Med Hangzhou Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Segmentation of the carotid intima-media (CIM) offers more precise morphological evidence for obesity and atherosclerotic disease compared to the method that measures its thickness and roughness during routine ultrasound scans. Although advanced deep learning technology has shown promise in enabling automatic and accurate medical image segmentation, the lack of a large quantity of high-quality CIM labels may hinder the model training process. Active learning (AL) tackles this issue by iteratively annotating the subset whose labels contribute the most to the training performance at each iteration. However, this approach substantially relies on the expert's experience, particularly when addressing ambiguous CIM boundaries that may be present in real-world ultrasound images. Our proposed approach, called pseudo-label divergence-based active learning (PLD-AL), aims to train segmentation models using a gradually enlarged and refined labeled pool. the approach has an outer and an inner loops: the outer loop calculates the Kullback-Leibler (KL) divergence of predictive pseudo-labels related to two consecutive AL iterations. It determines which portion of the unlabeled pool should be annotated by an expert. the inner loop trains two networks: the student network is fully trained on the current labeled pool, while the teacher network is weighted upon itself and the student one, ultimately refining the labeled pool. We evaluated our approach using both the Carotid Ultrasound Boundary Study dataset and an in-house dataset from Children's Hospital, Zhejiang University School of Medicine. Our results demonstrate that our approach outperforms state-of-the-art AL approaches. Furthermore, the visualization results show that our approach less overestimates the CIM area than the rest methods, especially for severely ambiguous ultrasound images at the thickness direction.

关键词： Carotid intima-media complex active learning image segmentation

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Unsupervised Deformable image Registration with Absent Correspondences in Pre-operative and Post-recurrence Brain Tumor MRI Scans 25th

Unsupervised Deformable Image Registration with Absent Corre...

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

作者： Mok, Tony C. W. Chung, Albert C. S. Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Registration of pre-operative and post-recurrence brain images is often needed to evaluate the effectiveness of brain gliomas treatment. While recent deep learning-based deformable registration methods have achieved remarkable success with healthy brain images, most of them would be unable to accurately align images with pathologies due to the absent correspondences in the reference image. In this paper, we propose a deep learning-based deformable registration method that jointly estimates regions with absent correspondence and bidirectional deformation fields. A forward-backward consistency constraint is used to aid in the localization of the resection and recurrence region from voxels with absence correspondences in the two images. Results on 3D clinical data from the BraTS-Reg challenge demonstrate our method can improve image alignment compared to traditional and deep learning-based registration approaches with or without cost function masking strategy. the source code is available at https://***/cwmok/DIRAC.

关键词： Absent correspondences Patient-specific registration Deformable registration

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computing Assessment for Liver Fibrosis Staging Using Real-World MR images 1st

Computing Assessment for Liver Fibrosis Staging Using Real-...

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作者： Zhang, Hanxiao Zhang, Minghui You, Xin Gu, Yun Yang, Guang-Zhong Shanghai Key Laboratory of Flexible Medical Robotics Tongren Hospital Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031870088

the accurate liver segmentation and fibrosis staging are of great importance in the diagnosis of liver disease and the subsequent treatment planning. To quantify and analyze liver fibrosis, the CARE 2024 challenge proposes a multi-task track, called LiQA, which aims to automatically segment the liver from single-phase MRI scan (LiSeg task) and predict liver fibrosis staging based on multi-phase and multi-center MRI scans (LiFS task). Both the LiSeg and LiFS tasks are subject to domain shifts. Furthermore, the LiFS task may encounter challenges, including the random absence of sequences for certain patients and misalignment among multi-phase MRIs. To address these challenges, we put forward a learning-based method for liver segmentation that utilizes both extended labeled and unlabeled data. In order to address the challenges posed by the LiFS task, we have developed a three-step approach. this approach includes the cropping of liver VOIs, the identification of liver fibrosis staging using CAM regularization and the integration of multi-sequence results. the efficacy of our solution was evaluated using the LiQA validation and testing set, and our team achieved the best performance award in the CARE-LiQA challenge. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Integration testing

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ChrSNet: Chromosome Straightening Using Self-attention Guided Networks 25th

ChrSNet: Chromosome Straightening Using Self-attention Guide...

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

作者： Zheng, Sunyi Li, Jingxiong Shui, Zhongyi Zhu, Chenglu Zhang, Yunlong Chen, Pingyi Yang, Lin Westlake Univ Sch Engn Artificial Intelligence & Biomed Image Anal Lab Hangzhou Peoples R China Westlake Inst Adv Study Inst Adv Technol Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9783031164408;9783031164392

Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attention guided framework to erase the curvature of chromosomes. the proposed framework extracts spatial information and local textures to preserve banding patterns in a regression module. With complementary information from the bent chromosome, a refinement module is designed to further improve fine details. In addition, we propose two dedicated geometric constraints to maintain the length and restore the distortion of chromosomes. To train our framework, we create a synthetic dataset where curved chromosomes are generated from the real-world straight chromosomes by grid-deformation. Quantitative and qualitative experiments are conducted on synthetic and real-world data. Experimental results show that our proposed method can effectively straighten bent chromosomes while keeping banding details and length.

关键词： Karyotyping Chromosome straightening Self-attention Microscopy

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Cross-Modality image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

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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： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. this work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. this can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. this accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

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RadimageGAN – A Multi-modal Dataset-Scale Generative AI for medical Imaging 3rd

RadImageGAN – A Multi-modal Dataset-Scale Generative AI for...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Liu, Zelong Smith, Peyton Lautin, Alexander Zhou, Jieshen Yoo, Maxwell Sullivan, Mikey Li, Haorun Deyer, Louisa Zhou, Alexander Yang, Arnold Yimaz, Alara Zhang, Catherine Grant, James Li, Daiqing Fayad, Zahi A. Huver, Sean Deyer, Timothy Mei, Xueyan BioMedical Engineering and Imaging Institute Icahn School of Medicine at Mount Sinai New YorkNY United States NVIDIA Santa ClaraCA United States East River Medical Imaging New YorkNY United States Department of Radiology Cornell Medicine New YorkNY United States Windreich Department of Artificial Intelligence and Human Health Icahn School of Medicine at Mount Sinai New YorkNY United States

ISBN: (纸本)9783031820069

Deep learning in medical imaging often requires large-scale, high-quality data, or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadimageNet is highly resource-intensive. To address these limitations, we introduce RadimageGAN, a multi-modal medical image generator developed by training StyleGAN-XL on the RadimageNet dataset of CT and MRI images and the HyperKvasir dataset of gastrointestinal images. RadimageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we developed the RadimageGAN-Labeler, which can generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. Using synthetic auto-labeled data from RadimageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data. We find that RadimageGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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