Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation
Friedrich-Alexander-University Erlangen-Nuremberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡分校）；Institut für Nanotechnologie und korrelative Mikroskopie（纳米技术与相关显微学研究所）；Flemish Institute for Technological Research（佛兰德技术研究所）；Luxembourg Institute of Science and Technology（卢森堡科学技术研究所）；Fraunhofer Institute for Ceramic Technologies and Systems（弗劳恩霍夫陶瓷技术与系统研究所）
Leonid Mill, David Wolff, Nele Gerrits, Patrick Philipp, Lasse Kling, Florian Vollnhals, Andrew Ignatenko, Christian Jaremenko, Yixing Huang, Olivier De Castro,… 展开作者
Leonid Mill, David Wolff, Nele Gerrits, Patrick Philipp, Lasse Kling, Florian Vollnhals, Andrew Ignatenko, Christian Jaremenko, Yixing Huang, Olivier De Castro, Jean-Nicolas Audinot, Inge Nelissen, Tom Wirtz, Andreas Maier, Silke Christiansen
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as e.g. size, shape and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, we present an elegant, flexible and versatile method to bypass this costly and tedious data acquisition process. We show that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, we derive a segmentation accuracy that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which we chose as examples. Our study paves the way towards the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as microscopies and spectroscopies, for a wide variety of studies and applications, including the detection of plastic micro- and nanoparticles.
Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
Center for Advanced Systems Understanding (CASUS)（高级系统理解中心）；Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；Institute of Computer Science, University of Wrocław（弗罗茨瓦夫大学计算机科学研究所）；Cluster of Excellence Physics of Life, TU Dresden（德累斯顿工业大学生命物理卓越集群）
Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and Annotation Framework
USC Institute for Creative Technologies（南加州大学创意技术研究所）；Arizona State University（亚利桑那州立大学）；Colorado School of Mines（科罗拉多矿业大学）；University of Southern California（南加州大学）
DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling
Department Artificial Intelligence in Biomedical Engineering（人工智能生物医学工程部门）；Friedrich-Alexander-University Erlangen-Nürnberg（埃朗根-纽伦堡弗里德里希-亚历山大大学）；Pattern Recognition Lab（模式识别实验室）；Institute for Nanotechnology and Correlative Microscopy（纳米技术与相关显微镜研究所）；Lucid Concepts AG（Lucid Concepts公司）；Fraunhofer Institute for Ceramic Technologies and Systems（弗劳恩霍夫陶瓷技术与系统研究所）；Freie Universität Berlin（柏林自由大学）；Max Planck Institute for the Science of Light（马克斯·普朗克光科学研究所）；Julius-Maximilians-University Würzburg（乌尔姆-马克斯·普朗克大学）
SSR-2D: Semantic 3D Scene Reconstruction from 2D Images
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；College of Electronic Science and Technology（电子科学与技术学院）；Xiangjiang Laboratory（湘江实验室）
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Microsoft Research（微软研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Minnesota（明尼苏达大学）；Stanford University（斯坦福大学）；Duke University（杜克大学）；New York University（纽约大学）
