DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
Ilya Belevich,Eija Jokitalo +1 more
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TLDR
DeepMIB as mentioned in this paper is a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation, which is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows.Abstract:
We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets with isotropic and anisotropic voxels. We distribute DeepMIB as both an open-source multi-platform Matlab code and as compiled standalone application for Windows, MacOS and Linux. It comes in a single package that is simple to install and use as it does not require knowledge of programming. DeepMIB is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows.read more
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Journal ArticleDOI
Democratising deep learning for microscopy with ZeroCostDL4Mic.
Lucas von Chamier,Romain F. Laine,Romain F. Laine,Johanna Jukkala,Christoph Spahn,Daniel Krentzel,Daniel Krentzel,Elias Nehme,Martina Lerche,Sara Hernández-Pérez,Sara Hernández-Pérez,Pieta K. Mattila,Pieta K. Mattila,Eleni Karinou,Seamus Holden,Ahmet Can Solak,Alexander Krull,Tim-Oliver Buchholz,Martin L. Jones,Loic Royer,Christophe Leterrier,Yoav Shechtman,Florian Jug,Mike Heilemann,Guillaume Jacquemet,Ricardo Henriques,Ricardo Henriques,Ricardo Henriques +27 more
TL;DR: ZeroCostDL4Mic as discussed by the authors is an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab, which allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation, object detection, denoising, and image-to-image translation.
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Avoiding a replication crisis in deep-learning-based bioimage analysis.
Romain F. Laine,Romain F. Laine,Ignacio Arganda-Carreras,Ignacio Arganda-Carreras,Ignacio Arganda-Carreras,Ricardo Henriques,Ricardo Henriques,Ricardo Henriques,Guillaume Jacquemet +8 more
TL;DR: In this article, the authors discuss key concepts that are important for researchers to consider when using deep learning for their microscopy studies and suggest what aspects of a deep learning analysis should be reported in publications to ensure reproducibility.
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SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and Scales
Avery H. Pennington,Oliver N. King,Win Min Tun,Elaine M.L. Ho,Imanol Luengo,Michele C. Darrow,Mark Basham +6 more
TL;DR: The SuRVoS application has been updated and redesigned to provide access to both manual and machine learning-based segmentation and annotation techniques, including support for crowd sourced data.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
TL;DR: Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.
Proceedings ArticleDOI
Best practices for convolutional neural networks applied to visual document analysis
TL;DR: A set of concrete bestpractices that document analysis researchers can use to get good results with neural networks, including a simple "do-it-yourself" implementation of convolution with a flexible architecture suitable for many visual document problems.
Journal ArticleDOI
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Alexander Mathis,Pranav Mamidanna,Kevin M. Cury,Taiga Abe,Venkatesh N. Murthy,Mackenzie W. Mathis,Mackenzie W. Mathis,Matthias Bethge +7 more
TL;DR: Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.
Journal ArticleDOI
ilastik: interactive machine learning for (bio)image analysis.
Stuart Berg,Dominik Kutra,Thorben Kroeger,Christoph N. Straehle,Bernhard X. Kausler,Carsten Haubold,Martin Schiegg,Janez Ales,Thorsten Beier,Markus Rudy,Kemal Eren,Jaime I Cervantes,Buote Xu,Fynn Beuttenmueller,Adrian Wolny,Chong Zhang,Ullrich Koethe,Fred A. Hamprecht,Anna Kreshuk +18 more
TL;DR: Ilastik as mentioned in this paper is an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise, which contains pre-defined workflows for image segmentation, object classification, counting and tracking.