DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation.
DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation.
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DeepMIB：用户友好的开源软件，用于训练生物图像分割的深度学习网络。
DOI：
10.1371/journal.pcbi.1008374
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Jokitalo E
中科院分区：
文献类型：
--
作者：
Belevich I;Jokitalo E
我们提出DeepMIB，一个新的软件包，能够训练卷积神经网络分割多维显微镜数据集在任何工作站。我们展示了它在二维和三维电子和多色光学显微镜数据集的各向同性和各向异性体素分割中的成功应用。我们将DeepMIB作为开源多平台Matlab代码和编译的独立应用程序分发给Windows， MacOS和Linux。它是一个简单的软件包，安装和使用，因为它不需要编程知识。DeepMIB适合每个有兴趣将深度学习的力量带入自己的图像分割工作流程的人。深度学习方法在处理大量收集的数据集方面受到高度追捧，并有望成为成像工作流程的重要组成部分。然而，在大多数情况下，深度学习仍然被认为是一项复杂的任务，只有图像分析专家才能掌握。通过DeepMIB，我们解决了这个问题，并为社区提供了一个用户友好的开源工具来训练卷积神经网络，并将其应用于分割2D和3D灰度或多色数据集。
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. Deep learning approaches are highly sought after solutions for coping with large amounts of collected datasets and are expected to become an essential part of imaging workflows. However, in most cases, deep learning is still considered as a complex task that only image analysis experts can master. With DeepMIB we address this problem and provide the community with a user-friendly and open-source tool to train convolutional neural networks and apply them to segment 2D and 3D grayscale or multi-color datasets.
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DOI：
10.1083/jcb.201004104
发表时间：
2010-05-31
期刊：
The Journal of cell biology
影响因子：
--
作者：
Linkert M;Rueden CT;Allan C;Burel JM;Moore W;Patterson A;Loranger B;Moore J;Neves C;Macdonald D;Tarkowska A;Sticco C;Hill E;Rossner M;Eliceiri KW;Swedlow JR
通讯作者：
Swedlow JR
影响因子：
9.8
作者：
McQuin C;Goodman A;Chernyshev V;Kamentsky L;Cimini BA;Karhohs KW;Doan M;Ding L;Rafelski SM;Thirstrup D;Wiegraebe W;Singh S;Becker T;Caicedo JC;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
25
作者：
Mathis, Alexander;Mamidanna, Pranav;Bethge, Matthias
通讯作者：
Bethge, Matthias
影响因子：
46.9
作者：
Sullivan, Devin P.;Winsnes, Casper F.;Lundberg, Emma
通讯作者：
Lundberg, Emma
影响因子：
5.8
作者：
Arganda-Carreras, Ignacio;Kaynig, Verena;Seung, H. Sebastian
通讯作者：
Seung, H. Sebastian