The segmentation of plant roots from soil and other growing media in X-ray computed tomography images is needed to effectively study the root system architecture without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. In this paper, we describe a method based on level sets and specifically adapted for this segmentation problem. In particular, we deal with the issues of using a level sets approach on large image volumes for root segmentation, and track active regions of the front using an occupancy grid. This method allows for straightforward modifications to a narrow-band algorithm such that excessive forward and backward movements of the front can be avoided, distance map computations in a narrow band context can be done in linear time through modification of Meijster et al.'s distance transform algorithm, and regions of the image volume are iteratively used to estimate distributions for root versus non-root classes. Results are shown of three plant species of different maturity levels, grown in three different media. Our method compares favorably to a state-of-the-art method for root segmentation in X-ray CT image volumes.
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
Medical University Vienna（维也纳医科大学）；Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University Vienna, Austria（维也纳医科大学生物医学成像与影像引导治疗系计算成像研究实验室）；Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University Vienna, Austria（维也纳医科大学眼科与视光学系眼科图像分析基督教多普勒实验室）；Department of Ophthalmology and Optometry, Medical University Vienna, Austria（维也纳医科大学眼科与视光学系）
Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria（人工智能研究所、医学数据科学中心、维也纳医学大学，奥地利）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心、维也纳医学大学，奥地利）；Department of Ophthalmology and Optometry, Medical University of Vienna, Austria（眼科与视光学部、维也纳医学大学，奥地利）；Laboratory for Ophthalmic Image Analysis, Medical University of Vienna, Austria（眼科图像分析实验室、维也纳医学大学，奥地利）
Patch-Based and Non-Patch-Based inputs Comparison into Deep Neural Models: Application for the Segmentation of Retinal Diseases on Optical Coherence Tomography Volumes
Laboratoire LIMOS, CNRS UMR 6158（利摩日大学实验室，法国国家科研中心UMR 6158联合研究单位）；Université Clermont-Auvergne（克莱蒙奥弗涅大学）；AL Mansour University College（曼苏尔大学学院）
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能信息处理重点实验室）；Suzhou Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院苏州智能计算技术研究院）；Beijing Jishuitan Hospital（北京积水潭医院）；School of Medicine, South China University of Technology（华南理工大学医学院）
Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review
University of Coimbra（科英布拉大学）；Erasmus MC（伊拉斯姆斯大学医学中心）；KU Leuven（鲁汶大学）；University of Porto（波尔图大学）；Centro Hospitalar e Universitário São João（圣若昂大学医院中心）；University Hospitals UZ Leuven（鲁汶大学医院）
Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Computer Science, University of Sydney（悉尼大学计算机学院）；Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院附属瑞金医院）
Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images
Medical University of Vienna（维也纳医科大学）；Carl Zeiss Meditec, Inc.（卡尔蔡司医疗技术公司）
视网膜成像数据中解剖特征的自动检测和定位与许多方面相关。在这项工作中，我们遵循以数据为中心的方法来优化分类器训练，用于在视网膜的光学相干断层扫描正面图像中进行视神经乳头检测和定位。我们研究领域知识驱动的空间复杂性降低对所产生的视神经乳头分割和定位性能的影响。我们提出了一种机器学习方法，用于在3D宽场扫频源光学相干断层扫描的2D正面投影中分割视神经乳头，该方法能够自动评估大量数据。对视网膜的手动注释的2D en face图像的评估表明，当底层像素级二进制分类任务通过领域知识在空间上放松时，标准U-Net的训练可以产生改进的视神经乳头分割和定位性能。
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images
School of Mathematics, University of Edinburgh（爱丁堡大学数学学院）；Institute for Regeneration and Repair, University of Edinburgh（爱丁堡大学再生与修复研究所）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Centre for Medical Informatics, University of Edinburgh（爱丁堡大学医学信息学中心）；Clinical Research Facility and Imaging, University of Edinburgh（爱丁堡大学临床研究与影像中心）；Department of Vision Sciences, Glasgow Caledonian University（格拉斯哥卡利多尼安大学视觉科学系）；British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh（爱丁堡大学英国心脏基金会心血管科学中心）；Centre for Clinical Brain Sciences, University of Edinburgh（爱丁堡大学临床脑科学中心）
Detection-aided liver lesion segmentation using deep learning
Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）；Eidgenössische Technische Hochschule Zürich (ETH Zürich)（苏黎世联邦理工学院）；Universitat Politècnica de Catalunya (UPC)（加泰罗尼亚理工大学）
Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；CentraleSupélec（巴黎中央理工-高等电力学院）；Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；Osaka University Graduate School of Medicine（大阪大学医学研究生院）；Ehime University Graduate School of Medicine（爱媛大学医学研究生院）
X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols
The University of Tokyo Hospital（东京大学医学部附属医院）；Center for Frontier Medical Engineering, Chiba University（千叶大学前沿医疗工程中心）；Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo（东京大学医学研究生院放射科与生物医学工程学系）
CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images
Queen Mary University of London（伦敦玛丽女王大学）；Communication University of Zhejiang（浙江传媒学院）；University of Leicester（莱斯特大学）；University of Electronic Science and Technology of China（电子科技大学）；Hangzhou Dianzi University（杭州电子科技大学）
Prediction of stent under-expansion in calcified coronary arteries using machine-learning on intravascular optical coherence tomography
Case Western Reserve University（凯斯西储大学）；The Hashemite University（哈希姆大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医学中心）；Florida Institute of Technology（佛罗里达理工学院）；University of South Florida（南佛罗里达大学）
在介入治疗前仔细评估支架扩张不足的风险将有助于治疗计划，包括应用支架前斑块修饰策略。在存在严重钙化的冠状动脉病变的情况下，实现适当的支架扩张仍然具有挑战性。基于我们在深度学习分割方面的工作，我们创建了一种自动化机器学习方法，该方法使用病变属性从支架植入前图像预测支架扩张不足，表明需要进行斑块修改。方法收集110个冠状动脉病变支架植入前后的血管内光学相干断层扫描图像数据。使用深度学习对支架植入前图像中的管腔和钙化进行分割，并提取每个病变的许多特征。我们分析了沿病变的支架扩张，从而实现了帧、节段和整个病变分析。我们训练回归模型来预测支架后管腔面积，然后计算支架扩张指数（SEI）。SEI ≥ 80%的支架< or >分别归类为“扩张不足”和“扩张良好”。结果当我们使用来自管腔和钙化的特征来训练高斯回归模型用于31帧片段长度的片段分析时，获得了最佳性能（均方根误差= 0.04 +/-0.02 mm 2，r = 0.94+/-0.04，p < 0.0001）。与其他方法相比，扩展不足分类结果（AUC=0.85+/-0.02）得到显著改善。结论：我们使用钙化和管腔特征来识别存在支架扩张不足风险的病变。结果表明，使用支架前图像可以告知医生需要应用斑块修饰方法。
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Charité - Universitätsmedizin Berlin（柏林查理医院）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；Radboud University Medical Center（拉德伯德大学医学中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Technical University of Munich（慕尼黑技术大学）
Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images
PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study
University of Florida（佛罗里达大学）；UniLaSalle Bauvais（UniLaSalle 博韦）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Missouri（密苏里大学）；University of Maine（缅因大学）；Argonne National Laboratory（阿贡国家实验室）
Segmenting Low-Contrast XCTs of Concrete: An Unsupervised Approach
Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague（机械系，土木工程学院，布拉格捷克技术大学）；Structural Composites Unit, Luxembourg Institute of Science and Technology (LIST)（结构复合材料单元，卢森堡科学与技术研究所（LIST））
Deep learning based automatic segmentation of lumbosacral nerves on non-contrast CT for radiographic evaluation: a pilot study
Spinal Pain Research Institute of Tongji University（同济大学脊柱疼痛研究所）；Brigham and Women's Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；School of Data and Computer Science, Sun Yat-sen University（中山大学数据科学与计算机学院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；Burke Neurological Institute（伯克神经学研究所）
