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Biopolymer segmentation from CLSM microscopy images using a convolutional neural network

, , , , and . Proceedings in applied mathematics and mechanics, 20 (1): e202000188 (2021)
DOI: 10.1002/pamm.202000274

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Computational 3D imaging to quantify structural components and assembly of protein networks, , , , and . Acta biomaterialia, (2018)Biopolymer segmentation from CLSM microscopy images using a convolutional neural network, , , , and . Proceedings in applied mathematics and mechanics, 20 (1): e202000188 (2021)Analysis of confocal microscopy image data of Physcomitrella chloroplasts to reveal adaptation principles leading to structural stability at the nanoscale, , , , and . PAMM, 16 (1): 69--70 (2016)Das Plastidenskelett: ein Ideengeber im Nanobereich, , , , and . Bionisch bauen : von der Natur lernen, Birkhäuser, Basel, (2019)The plastid skeleton: a source of ideas in the nano range, , , , and . Biomimetics for architecture : learning from nature, Birkhäuser, Basel, (2019)A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging, , , , , and . Computational and structural biotechnology journal, (2020)Cytological analysis and structural quantification of FtsZ1-2 and FtsZ2-1 network characteristics in Physcomitrella patens, , , , , and . Scientific reports, 8 (1): 11165 (2018)Feature-based Classification of Protein Networks using Confocal Microscopy Imaging and Machine Learning, , , , and . PAMM, 18 (1): e201800246 (2018)Cytological analysis and structural quantification of FtsZ1-2 and FtsZ2-1 network characteristics in Physcomitrella patens, , , , , and . Scientific Reports, (2018)Biopolymer segmentation from CLSM microscopy images using a convolutional neural network, , , , and . PAMM, 20 (1): e202000188 (2021)