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      Towards Automatic Embryo Staging in 3D+T Microscopy Images using Convolutional Neural Networks and PointNets

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          Abstract

          Automatic analyses and comparisons of different stages of embryonic development largely depend on a highly accurate spatio-temporal alignment of the investigated data sets. In this contribution, we compare multiple approaches to perform automatic staging of developing embryos that were imaged with time-resolved 3D light-sheet microscopy. The methods comprise image-based convolutional neural networks as well as an approach based on the PointNet architecture that directly operates on 3D point clouds of detected cell nuclei centroids. The proof-of-concept experiments with four wild-type zebrafish embryos render both approaches suitable for automatic staging with average deviations of 0.45 - 0.57 hours.

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          PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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            In Toto Imaging and Reconstruction of Post-Implantation Mouse Development at the Single-Cell Level

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              Volumetric and Multi-view CNNs for Object Classification on 3D Data

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                Author and article information

                Journal
                01 October 2019
                Article
                1910.00443
                c44ceb7f-b3c0-4dc8-a7d4-6c090262f1ce

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                5 pages, 3 figures, 1 table
                eess.IV cs.CV q-bio.CB

                Computer vision & Pattern recognition,Cell biology,Electrical engineering
                Computer vision & Pattern recognition, Cell biology, Electrical engineering

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