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

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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

Title: OpSeF : Open Source Python Framework for Collaborative Instance Segmentation of Bioimages
Author: Rasse, Tobias M.; Hollandi, Reka; Horvath, Peter
Contributor organization: Institute for Molecular Medicine Finland
University of Helsinki
Date: 2020-10-06
Language: eng
Number of pages: 15
Belongs to series: Frontiers in Bioengineering and Biotechnology
ISSN: 2296-4185
DOI: https://doi.org/10.3389/fbioe.2020.558880
URI: http://hdl.handle.net/10138/321197
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.
Subject: deep learning
biomedical image analysis
convolutional neural network
1182 Biochemistry, cell and molecular biology
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

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