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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： Qayyum, Abdul Mazher, Moona Niederer, Steven A. Faculty of Medicine National Heart and Lung Institute Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031870088

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, highlighting the need for precise diagnostic and therapeutic strategies. Whole heart segmentation (WHS) from medical images is vital for understanding cardiac anatomy, assessing disease progression, and guiding treatments. However, WHS poses significant challenges due to the dynamic shape changes of the heart during the cardiac cycle, motion artifacts, low contrast-to-noise ratio, and variability from multi-center data. Additionally, CT and MRI modalities each present unique difficulties: CT provides high spatial resolution but poor soft-tissue contrast, while MRI offers excellent soft-tissue contrast but is prone to artifacts. To address these challenges, we propose a two-stage method. In the first stage, the DINOv2 framework leverages a 3D Vision-LSTM (xLSTM) model for self-supervised learning on the limited, unlabeled WHS++ dataset to overcome the scarcity of annotated data. In the second stage, we fine-tune the model for segmentation by freezing the encoder and optimizing the decoder. Our approach is rigorously evaluated on the WHS++ dataset for both CT and MRI modalities, demonstrating superior performance compared to state-of-the-art deep learning models in accurately segmenting cardiac structures. Our proposed solution achieved an average Dice Coefficient of 0.9414 using CT and 0.8891 using MRI dataset. This work advances the field of cardiac imaging by providing a robust, generalizable solution for automated whole heart segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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Assessing Self-supervised xLSTM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications 1st

Assessing Self-supervised xLSTM-UNet Architectures for Head ...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th International Conference on medical image computing and computer-Assisted Intervention, MICCAI 2024

作者： Qayyum, Abdul Mazher, Moona Niederer, Steven A. National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031832734

Radiation therapy (RT) plays a pivotal role in treating head and neck cancer (HNC), with MRI-guided approaches offering superior soft tissue con- trast and daily adaptive capabilities that significantly enhance treatment preci- sion while minimizing side effects. To optimize MRI-guided adaptive RT for HNC, we propose a novel two-stage model for Head and Neck Tumor Segmenta- tion. In the first stage, we leverage a Self-Supervised 3D Student-Teacher Learn- ing Framework, specifically utilizing the DINOv2 architecture, to learn effec- tive representations from a limited unlabeled dataset. This approach effectively addresses the challenge posed by the scarcity of annotated data, enabling the model to generalize better in tumor identification and segmentation. In the second stage, we fine-tune an xLSTM-based UNet model that is specifically designed to capture both spatial and sequential features of tumor progression. This hybrid architecture improves segmentation accuracy by integrating temporal dependen- cies, making it particularly well-suited for MRI-guided adaptive RT planning in HNC. The model’s performance is rigorously evaluated on a diverse set of HNC cases, demonstrating significant improvements over state-of-the-art deep learning models in accurately segmenting tumor structures. Our proposed solu- tion achieved an impressive mean aggregated Dice Coefficient of 0.81 for pre- RT segments and 0.65 for mid-RT segments, underscoring its effectiveness in automated segmentation tasks. This work advances the field of HNC imaging by providing a robust, generalizable solution for automated Head and Neck Tumor Segmentation, ultimately enhancing the quality of care for patients undergo- ing RT. Our team name is DeepLearnAI (CEMRG). The code for this work is available at https://***/RespectKnowledge/SSL-based-DINOv2_Vision- LSTM_Head-and-Neck-Tumor_Segmentation. © The Author(s) 2025.

关键词： Self-supervised learning

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Effective Approach Based on Student-Teacher Self-supervised Deep Learning for Multi-class Bi-atrial Segmentation Challenge 15th

Effective Approach Based on Student-Teacher Self-supervised ...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Mazher, Moona Qayyum, Abdul Niederer, Steven A. Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom

ISBN: (纸本)9783031877551

Atrial fibrillation (AF), a widespread cardiac arrhythmia, is associated with significant morbidity and mortality. However, current treatments are limited due to an incomplete understanding of the atrial anatomy that drives AF. The MBAS 2024 Challenge builds on our 2018 left atrium challenge by expanding the focus to include both the left and right atria. Leveraging the largest bi-atrial LGE-MRI dataset to date, this challenge centers on advanced segmentation and biomarker identification to enhance treatment strategies for AF. The goal of this challenge is to develop automated medical semantic segmentation methods using a specialized 3-class bi-atrial dataset, ultimately aiming to improve the performance of AF treatments. We have introduced a novel two-stage process for the precise segmentation of the atrial walls and both atria. Our approach begins by utilizing self-supervised learning techniques tailored for 3D medical imaging, enabling the model to effectively capture the complex anatomical structures present in 3D medical datasets. Following this pre-training stage, the learned knowledge is transferred to a targeted 3D medical image segmentation task. In the second stage, we present an xLSTM-based model enhanced with the integration of Self-Supervised Learning (SSL) and cross-window attention mechanisms. This innovative approach has demonstrated superior performance in segmenting the atrial walls as well as the left and right blood pool cavities, offering a promising advancement in the accuracy and effectiveness of AF treatment through more detailed anatomical segmentation. Our approach achieved overall second ranked on validation leaderboard. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives

AI-assisted prostate cancer detection and localisation on bi...

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medical Imaging 2025: computer-Aided Diagnosis

作者： Wu, Xiangcen Wang, Yipei Yang, Qianye Thorley, Natasha Punwani, Shonit Kasivisvanathan, Veeru Bonmati, Ester Hu, Yipeng Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Div of Surgery & Interventional Sci University College London London United Kingdom School of Computer Science and Engineering University of Westminster London United Kingdom

ISBN: (纸本)9781510685925

Prostate cancer diagnosis through MR imaging have currently relied on radiologists’ interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to develop deep learning models that improve the overall cancer diagnostic accuracy, by classifying radiologist-identified patients or lesions (i.e. radiologist-positives), as opposed to the existing models that are trained to discriminate over all patients. We develop a single voxel-level classification model, with a simple percentage threshold to determine positive cases, at levels of lesions, Barzell-zones and patients. Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging. Among varying definition of clinical significance, the proposed strategy, for example, achieved a specificity of 44.1% (with AI assistance) from 36.3% (by radiologists alone), at a controlled sensitivity of 80.0% on the publicly available UCLA data set. This provides measurable clinical values in a range of applications such as reducing unnecessary biopsies, lowering cost in cancer screening and quantifying risk in therapies. © 2025 SPIE.

关键词： Biopsy

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Tackling Hallucination from Conditional Models for medical image Reconstruction with DynamicDPS

arXiv

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arXiv 2025年

作者： Kim, Seunghoi Tregidgo, Henry F.J. Figini, Matteo Jin, Chen Joshi, Sarang Alexander, Daniel C. Centre for Medical Image Computing University College London United Kingdom Department of Medical Physics and Biomedical Engineering University College London United Kingdom Department of Computer Science University College London United Kingdom The University of Utah United States Centre for AI DS&AI AstraZeneca United Kingdom

Hallucinations are spurious structures not present in the ground truth, posing a critical challenge in medical image reconstruction, especially for data-driven conditional models. We hypothesize that combining an unconditional diffusion model with data consistency, trained on a diverse dataset, can reduce these hallucinations. Based on this, we propose DynamicDPS, a diffusion-based framework that integrates conditional and unconditional diffusion models to enhance low-quality medical images while systematically reducing hallucinations. Our approach first generates an initial reconstruction using a conditional model, then refines it with an adaptive diffusion-based inverse problem solver. DynamicDPS skips early stage in the reverse process by selecting an optimal starting time point per sample and applies Wolfe’s line search for adaptive step sizes, improving both efficiency and image fidelity. Using diffusion priors and data consistency, our method effectively reduces hallucinations from any conditional model output. We validate its effectiveness in image Quality Transfer for low-field MRI enhancement. Extensive evaluations on synthetic and real MR scans, including a downstream task for tissue volume estimation, show that DynamicDPS reduces hallucinations, improving relative volume estimation by over 15% for critical tissues while using only 5% of the sampling steps required by baseline diffusion models. As a model-agnostic and fine-tuning-free approach, DynamicDPS offers a robust solution for hallucination reduction in medical imaging. The code will be made publicly available upon publication. Copyright © 2025, The Authors. All rights reserved.

关键词： Inverse problems

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Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion

arXiv

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arXiv 2025年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Viale Andrea Doria 6 Catania Italy Centre for Medical Image Computing University College London 90 High Holborn London United Kingdom MIFT Department University of Messina Viale Ferdinando Stagno d’Alcontres 31 Messina Italy

The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it possible to predict future medical scans for individual patients. However, despite significant advancements in AI, current methods continue to face challenges including achieving patient-specific individualization, ensuring spatiotemporal consistency, efficiently utilizing longitudinal data, and managing the substantial memory demands of 3D scans. To address these challenges, we propose Brain Latent Progression (BrLP), a novel spatiotemporal model designed to predict individual-level disease progression in 3D brain MRIs. The key contributions in BrLP are fourfold: (i) it operates in a small latent space, mitigating the computational challenges posed by high-dimensional imaging data;(ii) it explicitly integrates subject metadata to enhance the individualization of predictions;(iii) it incorporates prior knowledge of disease dynamics through an auxiliary model, facilitating the integration of longitudinal data;and (iv) it introduces the Latent Average Stabilization (LAS) algorithm, which (a) enforces spatiotemporal consistency in the predicted progression at inference time and (b) allows us to derive a measure of the uncertainty for the prediction. We train and evaluate BrLP on 11,730 T1-weighted (T1w) brain MRIs from 2,805 subjects and validate its generalizability on an external test set comprising 2,257 MRIs from 962 subjects. Our experiments compare BrLP-generated MRI scans with real follow-up MRIs, demonstrating state-of-the-art accuracy compared to existing methods. The code is publicly available at: https://***/LemuelPuglisi/BrLP. © 2025, CC BY.

关键词： Magnetic resonance imaging

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Combined impact of grey and superficial white matter abnormalities: implications for epilepsy surgery

arXiv

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arXiv 2025年

作者： Kozma, Csaba Horsley, Jonathan Hall, Gerard Simpson, Callum de Tisi, Jane Miserocchi, Anna McEvoy, Andrew W. Vos, Sjoerd B. Winston, Gavin P. Wang, Yujiang Duncan, John S. Taylor, Peter N. School of Computing Newcastle University Newcastle upon Tyne United Kingdom Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle upon Tyne United Kingdom Department of Clinical and Experimental Epilepsy UCL Queen Square Institute of Neurology University College London London United Kingdom Western Australia National Imaging Facility The University of Western Australia Nedlands Australia Centre for Medical Image Computing Computer Science Department University College London London United Kingdom Department of Medicine Queen’s University KingstonON Canada

Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (GM) and superficial white matter (SWM). However, it is unknown if both types of abnormalities are important in supporting seizures. Here, we test if surgical removal of GM and/or SWM abnormalities relates to post-surgical seizure outcome in people with temporal lobe epilepsy (TLE). We analyzed structural imaging data from 143 TLE patients (pre-op dMRI and pre-op T1-weighted MRI) and 97 healthy controls. We calculated GM volume abnormalities and SWM mean diffusivity abnormalities and evaluated if their surgical removal distinguished seizure outcome groups post-surgically. At a group level, GM and SWM abnormalities were most common in the ipsilateral temporal lobe and hippocampus in people with TLE. Analyzing both modalities together, compared to in isolation, improved surgical outcome discrimination (GM AUC = 0.68, p 1,2 outcomes. These findings suggest that regions identified as abnormal from both diffusion-weighted and T1-weighted MRIs are involved in the epileptogenic network and that resection of both types of abnormalities may enhance the chances of living without disabling seizures. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Beyond crossing fibers: Tractography exploiting sub-voxel fibre dispersion and neighbourhood structure

Beyond crossing fibers: Tractography exploiting sub-voxel fi...

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23rd International Conference on Information Processing in medical Imaging, IPMI 2013

作者： Rowe, Matthew Zhang, Hui Gary Oxtoby, Neil Alexander, Daniel C. Centre for Medical Image Computing Department of Computer Science University College London United Kingdom

ISBN: (纸本)9783642388675

In this paper we propose a novel algorithm which leverages models of white matter fibre dispersion to improve tractography. Tractography methods exploit directional information from diffusion weighted magnetic resonance (DW-MR) imaging to infer connectivity between different brain regions. Most tractography methods use a single direction (e.g. the principal eigenvector of the diffusion tensor) or a small set of discrete directions (e.g. from the peaks of an orientation distribution function) to guide streamline propagation. This strategy ignores the effects of within-bundle orientation dispersion, which arises from fanning or bending at the sub-voxel scale, and can lead to missing connections. Various recent DW-MR imaging techniques estimate the fibre dispersion in each bundle directly and model it as a continuous distribution. Here we introduce an algorithm to exploit this information to improve tractography. The algorithm further uses a particle filter to probe local neighbourhood structure during streamline propagation. Using information gathered from neighbourhood structure enables the algorithm to resolve ambiguities between converging and diverging fanning structures, which cannot be distinguished from isolated orientation distribution functions. We demonstrate the advantages of the new approach in synthetic experiments and in vivo data. Synthetic experiments demonstrate the effectiveness of the particle filter in gathering and exploiting neighbourhood information in recovering various canonical fibre configurations and experiments with in vivo brain data demonstrate the advantages of utilising dispersion in tractography, providing benefits in practical situations. © 2013 Springer-Verlag.

关键词： Magnetic resonance imaging

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： image segmentation

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A simulation framework for quantitative validation of artefact correction in diffusion MRI 24th

A simulation framework for quantitative validation of artefa...

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24th International Conference on Information Processing in medical Imaging, IPMI 2015

作者： Graham, Mark S. Drobnjak, Ivana Zhang, Hui Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. The ultimate aim of these techniques is improved image alignment, yet in the literature this is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We demonstrate the application of this framework by testing one of the most commonly used methods for EC correction, registration of DWIs to b=0, and reveal the systematic bias this introduces into corrected datasets. © Springer International Publishing Switzerland 2015.

关键词： Magnetic resonance imaging

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