The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is -- rather than improving the segmentation quality compared to the baseline method -- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitavely on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
University of Kaiserslautern（凯泽斯劳滕大学）；Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (The Swiss AI Lab IDSIA)（达莱莫勒人工智能研究所（瑞士人工智能实验室IDSIA））；Scuola universitaria professionale della Svizzera italiana (SUPSI)（瑞士意大利语区应用科学大学（SUPSI））；Universitá della Svizzera italiana (USI)（瑞士意大利语区大学（USI））；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training
Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg（医学物理，诊断与介入放射科，弗赖堡大学医学中心，医学院，弗赖堡大学）；University of Freiburg（弗赖堡大学）；ELLIS Institute Tübingen（图宾根ELLIS研究所）
Semantic Segmentation of highly class imbalanced fully labelled 3D volumetric biomedical images and unsupervised Domain Adaptation of the pre-trained Segmentation Network to segment another fully unlabelled Biomedical 3D Image stack
Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Imaging, German Cancer Research Center (DKFZ)（亥姆霍兹影像，德国癌症研究中心）；University of Heidelberg（海德堡大学）；ETH Zürich（苏黎世联邦理工学院）；Heidelberg University Hospital（海德堡大学医院）；National Center for Tumor Diseases (NCT) Heidelberg（海德堡国家肿瘤疾病中心）
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院生物工程研究所）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）；Institut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon（里昂高等师范学院里昂功能基因组学研究所）；Centre National de la Recherche Scientifique (CNRS)（法国国家科学研究中心）
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill（放射科与生物医学研究成像中心（BRIC）、北卡罗来纳大学教堂山分校）；Lampe Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University（Lampe联合生物医学工程部门、北卡罗来纳大学教堂山分校和北卡罗来纳州立大学）
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；University Hospital of Marburg（马尔堡大学医院）；Isomics, Inc.（Isomics公司）；GE Research（通用电气研究）
