OpSeF : Open Source Python Framework for Collaborative Instance Segmentation of Bioimages

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dc.contributor.author Rasse, Tobias M.
dc.contributor.author Hollandi, Reka
dc.contributor.author Horvath, Peter
dc.date.accessioned 2020-11-09T06:29:01Z
dc.date.available 2020-11-09T06:29:01Z
dc.date.issued 2020-10-06
dc.identifier.citation Rasse , T M , Hollandi , R & Horvath , P 2020 , ' OpSeF : Open Source Python Framework for Collaborative Instance Segmentation of Bioimages ' , Frontiers in Bioengineering and Biotechnology , vol. 8 , 558880 . https://doi.org/10.3389/fbioe.2020.558880
dc.identifier.other PURE: 150754183
dc.identifier.other PURE UUID: 5e3a5892-4064-43c2-842c-1a9038715a68
dc.identifier.other WOS: 000579838000001
dc.identifier.uri http://hdl.handle.net/10138/321197
dc.description.abstract Various pre-trained deep learning models for the segmentation of bioimages have been made available as developer-to-end-user solutions. They are optimized for ease of use and usually require neither knowledge of machine learning nor coding skills. However, individually testing these tools is tedious and success is uncertain. Here, we present the Open Segmentation Framework (OpSeF), a Python framework for deep learning-based instance segmentation. OpSeF aims at facilitating the collaboration of biomedical users with experienced image analysts. It builds on the analysts' knowledge in Python, machine learning, and workflow design to solve complex analysis tasks at any scale in a reproducible, well-documented way. OpSeF defines standard inputs and outputs, thereby facilitating modular workflow design and interoperability with other software. Users play an important role in problem definition, quality control, and manual refinement of results. OpSeF semi-automates preprocessing, convolutional neural network (CNN)-based segmentation in 2D or 3D, and postprocessing. It facilitates benchmarking of multiple models in parallel. OpSeF streamlines the optimization of parameters for pre- and postprocessing such, that an available model may frequently be used without retraining. Even if sufficiently good results are not achievable with this approach, intermediate results can inform the analysts in the selection of the most promising CNN-architecture in which the biomedical user might invest the effort of manually labeling training data. We provide Jupyter notebooks that document sample workflows based on various image collections. Analysts may find these notebooks useful to illustrate common segmentation challenges, as they prepare the advanced user for gradually taking over some of their tasks and completing their projects independently. The notebooks may also be used to explore the analysis options available within OpSeF in an interactive way and to document and share final workflows. Currently, three mechanistically distinct CNN-based segmentation methods, the U-Net implementation used in Cellprofiler 3.0, StarDist, and Cellpose have been integrated within OpSeF. The addition of new networks requires little; the addition of new models requires no coding skills. Thus, OpSeF might soon become both an interactive model repository, in which pre-trained models might be shared, evaluated, and reused with ease. en
dc.format.extent 15
dc.language.iso eng
dc.relation.ispartof Frontiers in Bioengineering and Biotechnology
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject deep learning
dc.subject biomedical image analysis
dc.subject segmentation
dc.subject convolutional neural network
dc.subject U-net
dc.subject cellpose
dc.subject StarDist
dc.subject python
dc.subject NUCLEUS SEGMENTATION
dc.subject IMAGE
dc.subject MICROSCOPY
dc.subject PLATFORM
dc.subject 1182 Biochemistry, cell and molecular biology
dc.subject 113 Computer and information sciences
dc.title OpSeF : Open Source Python Framework for Collaborative Instance Segmentation of Bioimages en
dc.type Article
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization University of Helsinki
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.3389/fbioe.2020.558880
dc.relation.issn 2296-4185
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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