Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
PLoS Comput Biol. 2021 Mar 2;17(3):e1008374. doi: 10.1371/journal.pcbi.1008374. eCollection 2021 Mar.
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.
我们介绍了 DeepMIB,这是一个新的软件包,能够在任何工作站上训练用于分割多维显微镜数据集的卷积神经网络。我们成功地将其应用于具有各向同性和各向异性体素的 2D 和 3D 电子和多色荧光显微镜数据集的分割。我们将 DeepMIB 作为开源的多平台 Matlab 代码以及适用于 Windows、MacOS 和 Linux 的编译独立应用程序分发。它是一个单一的软件包,易于安装和使用,因为它不需要编程知识。DeepMIB 适用于所有有兴趣将深度学习的强大功能引入自己的图像分割工作流程的人。