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

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          Abstract

          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.

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          Content-aware image restoration: pushing the limits of fluorescence microscopy

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            Deep learning

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              An Objective Comparison of Cell Tracking Algorithms

              We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell tracking algorithms. With twenty-one participating algorithms and a data repository consisting of thirteen datasets of various microscopy modalities, the challenge displays today’s state of the art in the field. We analyze the results using performance measures for segmentation and tracking that rank all participating methods. We also analyze the performance of all algorithms in terms of biological measures and their practical usability. Even though some methods score high in all technical aspects, not a single one obtains fully correct solutions. We show that methods that either take prior information into account using learning strategies or analyze cells in a global spatio-temporal video context perform better than other methods under the segmentation and tracking scenarios included in the challenge.
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                Author and article information

                Journal
                Nature Methods
                Nat Methods
                Springer Nature
                1548-7091
                1548-7105
                December 17 2018
                Article
                10.1038/s41592-018-0261-2
                30559429
                cbe54f0c-ac8d-4dad-a4c7-f03efe9076a1
                © 2018

                http://www.springer.com/tdm

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