Author Correction: U-Net: deep learning for cell counting, detection, and morphometry.

In the version of this paper originally published, one of the affiliations for Dominic Mai was incorrect: "Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany" should have been "Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany." This change required some renumbering of subsequent author affiliations. These corrections have been made in the PDF and HTML versions of the article, as well as in any cover sheets for associated Supplementary Information.

Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation.

The innate immune cell compartment is highly diverse in the healthy central nervous system (CNS), including parenchymal and non-parenchymal macrophages. However, this complexity is increased in inflammatory settings by the recruitment of circulating myeloid cells. It is unclear which disease-specific myeloid subsets exist and what their transcriptional profiles and dynamics during CNS pathology are. Combining deep single-cell transcriptome analysis, fate mapping, in vivo imaging, clonal analysis, and transgenic mouse lines, we comprehensively characterized unappreciated myeloid subsets in several CNS compartments during neuroinflammation. During inflammation, CNS macrophage subsets undergo self-renewal, and random proliferation shifts toward clonal expansion. Last, functional studies demonstrated that endogenous CNS tissue macrophages are redundant for antigen presentation. Our results highlight myeloid cell diversity and provide insights into the brain's innate immune system.

U-Net: deep learning for cell counting, detection, and morphometry.

U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.

A new fate mapping system reveals context-dependent random or clonal expansion of microglia.

Microglia constitute a highly specialized network of tissue-resident immune cells that is important for the control of tissue homeostasis and the resolution of diseases of the CNS. Little is known about how their spatial distribution is established and maintained in vivo. Here we establish a new multicolor fluorescence fate mapping system to monitor microglial dynamics during steady state and disease. Our findings suggest that microglia establish a dense network with regional differences, and the high regional turnover rates found challenge the universal concept of microglial longevity. Microglial self-renewal under steady state conditions constitutes a stochastic process. During pathology this randomness shifts to selected clonal microglial expansion. In the resolution phase, excess disease-associated microglia are removed by a dual mechanism of cell egress and apoptosis to re-establish the stable microglial network. This study unravels the dynamic yet discrete self-organization of mature microglia in the healthy and diseased CNS.

Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks.

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.