Ultrahigh-field (UHF) magnetic resonance imaging (MRI), i.e., 7T MRI, provides superior anatomical details of internal brain structures owing to its enhanced signal-to-noise ratio and susceptibility-induced contrast. However, the widespread use of 7T MRI is limited by its high cost and lower accessibility compared to low-field (LF) MRI. This study proposes a deep-learning framework that systematically fuses the input LF magnetic resonance feature representations with the inferred 7T-like feature representations for brain image segmentation tasks in a 7T-absent environment. Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features. Using intensity-guided features obtained from such aggregation and assimilation, segmentation models can recognize subtle structural representations that are usually difficult to recognize when relying only on LF features. Beyond such advantages, this strategy can seamlessly be utilized by modulating the contrast of LF features in alignment with UHF guidance, even when employing arbitrary segmentation models. Exhaustive experiments demonstrated that the proposed method significantly outperformed all baseline models on both brain tissue and whole-brain segmentation tasks; further, it exhibited remarkable adaptability and scalability by successfully integrating diverse segmentation models and tasks. These improvements were not only quantifiable but also visible in the superlative visual quality of segmentation masks.
Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this paper, we propose a prior knowledge guided and transferability based framework to select the best source tasks among a collection of brain image segmentation tasks, to improve the transfer learning performance on the given target task. The framework consists of modality analysis, RoI (region of interest) analysis, and transferability estimation, such that the source task selectio…
DINeuro: Distilling Knowledge from 2D Natural Images via Deformable Tubular Transferring Strategy for 3D Neuron Reconstruction
School of Computer Science, The University of Sydney（悉尼大学计算机学院）；SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University（东南大学SEU-ALLEN联合中心）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis
The Chinese University of Hong Kong (CUHK)（香港中文大学）；CUHK Shenzhen Research Institute（香港中文大学深圳研究院）；China Medical University（中国医科大学）；University College London（伦敦大学学院）；UCL Hawkes Institute（伦敦大学学院霍克斯研究所）
Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation（亨利杰克逊基金会神经科学与再生医学中心）；Radiology and Imaging Sciences, Clinical Center, National Institute of Health（美国国立卫生研究院临床中心放射与成像科学部）；Department of Computer Science, Middle Tennessee State University（中田纳西州立大学计算机科学系）；Department of Electrical Engineering, Vanderbilt University（范德堡大学电气工程系）；Department of Biomedical Engineering, Vanderbilt University（范德堡大学生物医学工程系）；Department of Computer Science, Vanderbilt University（范德堡大学计算机科学系）；Departments of Surgery, Neurosurgery, Hearing & Speech Sciences（范德堡大学医学中心外科、神经外科、听力与言语科学系；范德堡脑研究所健康服务研究中心；危重病、脑功能障碍及康复中心；田纳西谷退伍军人事务部医疗中心（美国退伍军人事务部）医学中心外科、神经外科、听力与言语科学系；范德堡脑研究所健康服务研究中心；危重病、脑功能障碍及康复中心；田纳西谷退伍军人事务部医疗中心）；Center for Health Services Research, Vanderbilt Brain Institute；Critical Illness, Brain Dysfunction, and Survivorship Center, Vanderbilt University Medical Center；VA Tennessee Valley Healthcare System, Department of Veterans Affairs Medical Center
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to transfer protected health information (PHI) between institutions. Additionally, due to the nature of some studies, there may not be a large public dataset available on which to train models. To address this conundrum, we analyze the efficacy of transferring the model itself in lieu of data between different sites. By doing so we accomplish two goals: 1) the mode…
A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所麦康奈尔脑成像中心）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想集团有限公司）
Liver cancer is one of the most common cancers worldwide. Due to inconspicuous texture changes of liver tumor, contrast-enhanced computed tomography (CT) imaging is effective for the diagnosis of liver cancer. In this paper, we focus on improving automated liver tumor segmentation by integrating multi-modal CT images. To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation. Different from existing multi-modal methods that fuse information from different modalities by a single model, with ML, an ensemble of modality-specific mo…
Hasso-Plattner-Institute, Potsdam University Germany（哈索·普拉特纳研究所，波茨坦大学（德国））；Digital Health & Machine Learning（数字健康与机器学习（研究方向，非独立机构，不提取））
Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields. In this work, we leverage these techniques, and we propose 3D versions for five different self-supervised methods, in the form of proxy tasks. Our methods facilitate neural network feature learning from unlabeled 3D images, aiming to reduce the required cost for expert annotation. The developed algorithms are 3D Contrastive Predictive Coding, 3D Rotation prediction, 3D Jigsaw puzzles, Relative 3D patch location, and 3D Exemplar networks. Our experiments show that …
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
