UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images
Autor: | Shigeyuki Oba, Shin Ishii, Hidetoshi Urakubo, Torsten Bullmann, Yoshiyuki Kubota |
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Rok vydání: | 2019 |
Předmět: |
0301 basic medicine
Ground truth Multidisciplinary Artificial neural network business.industry Computer science lcsh:R 3D reconstruction lcsh:Medicine Pattern recognition Image segmentation Convolutional neural network Neural circuits Article Visualization 03 medical and health sciences 030104 developmental biology 0302 clinical medicine lcsh:Q Segmentation Artificial intelligence lcsh:Science business 030217 neurology & neurosurgery Software |
Zdroj: | Scientific Reports, Vol 9, Iss 1, Pp 1-9 (2019) Scientific Reports |
Popis: | Recently, there has been a rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from a stack of two-dimensional (2D) electron microscopic (EM) images. The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for DNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, that enables 2D- and 3D-DNN-based segmentation for non-computer experts. UNI-EM is a software collection for DNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM comes with a set of 2D DNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D DNN-based neuron segmentation algorithm. The 2D- and 3D-DNNs are known to show state-of-the-art level segmentation performance. We then provided two-example workflows: mitochondria segmentation using a 2D DNN as well as neuron segmentation using FFNs. Following these example workflows, users can benefit from DNN-based segmentation without any knowledge of Python programming or DNN frameworks. |
Databáze: | OpenAIRE |
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