Automatic and Manual Segmentation of Hippocampus in Epileptic Patients MRI
Rutgers University（罗格斯大学）；Henry Ford Health System（亨利·福特医疗系统）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Spectrum Health System（光谱健康系统）；Michigan State University（密歇根州立大学）；University of Tehran（德黑兰大学）
The hippocampus is a seminal structure in the most common surgically-treated form of epilepsy. Accurate segmentation of the hippocampus aids in establishing asymmetry regarding size and signal characteristics in order to disclose the likely site of epileptogenicity. With sufficient refinement, it may ultimately aid in the avoidance of invasive monitoring with its expense and risk for the patient. To this end, a reliable and consistent method for segmentation of the hippocampus from magnetic resonance imaging (MRI) is needed. In this work, we present a systematic and statistical analysis approach for evaluation of automated segmentation methods in order to establish one that reliably approximates the results achieved by manual tracing of the hippocampus.
Distribution-Aware Replay for Continual MRI Segmentation
Technical University of Darmstadt（达姆施塔特工业大学）；Stanford University（斯坦福大学）；The Hessian Center for Artificial Intelligence: hessian.AI（黑森人工智能中心（hessian.AI））
Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization
University of Michigan-Shanghai Jiao Tong University Joint Institute（上海交通大学密西根学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术学院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
University College London（伦敦大学学院）；Basque Center on Cognition, Brain and Language（巴斯克认知、大脑与语言中心）；University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Technical University of Denmark（丹麦技术大学）
Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation
Istituto Nazionale di Fisica Nucleare（意大利国家核物理研究所）；Università degli Studi di Bari（巴里大学）；INAF - Astronomical Observatory of Capodimonte（意大利国家天体物理研究所-卡波迪蒙特天文台）；Università degli Studi di Genova（热那亚大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里克二世大学）；Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR（意大利国家研究委员会智能系统与自动化研究所）
Confidence Intervals for Performance Estimates in Brain MRI Segmentation
Sorbonne Université（索邦大学）；Institut du Cerveau – Paris Brain Institute - ICM（大脑研究所 – 巴黎脑研究所 - ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公立医院集团）；Hôpital de la Pitié-Salpêtrière（皮蒂-萨尔皮特里医院）；Soda team, Inria（法国国家信息与自动化研究所Soda团队）
Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（集成空天地海大数据应用技术国家工程实验室，计算机科学与工程学院，西北工业大学）
Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
Darmstadt University of Technology（达姆施塔特工业大学）
在使用深度学习执行医学图像分割时，访问适当的基础设施至关重要。这一要求使得在资源有限的情况下，如农村地区和危机期间的初级保健设施，很难运行最先进的细分模型。最近新兴的神经元胞自动机（NCA）领域已经表明，局部相互作用的单细胞模型可以在低分辨率输入的图像生成或分割等任务中取得有竞争力的结果。然而，它们受到高VRAM要求和难以达到高分辨率图像的收敛的限制。为了克服这些限制，我们提出了Med-NCA，一个用于高分辨率图像分割的端到端NCA训练管道。我们的方法遵循两个步骤的过程。全局知识首先在缩小的图像中的细胞之间进行通信。之后，执行基于块的分割。我们提出的Med-NCA在海马和前列腺分割方面分别比经典的UNet好2%和3%，同时也小了500倍。我们还表明，Med-NCA是通过设计不变的图像尺度，形状和平移，经历只有轻微的性能下降，即使有很强的转变，是强大的MRI采集伪影。Med-NCA甚至可以在Raspberry Pi B+上实现高分辨率医学图像分割，Raspberry Pi B+可以说是能够运行PyTorch的最小设备，并且可以由标准电源供电。
