Abstract:Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscop… Show more
“…The segmentation accuracy was sufficiently high without any proofreading (Fig. 2C, bottom and right panel; RAND score: 0.85; see Methods), as expected from published results on 2D CNN-based segmentation 31,32 . The detailed instructions for the mitochondria segmentation task can be found at the public repository GitHub (see Data availability).Figure 3Performance survey in 2D CNN-based segmentation of neurons, synapses, and mitochondria.…”
Section: Resultssupporting
confidence: 82%
“…3D; ~0.3 RAND score). The accuracy of mitochondria segmentation in a standard CNN (network topology: ResNet; loss function: least square; number of layers: 9; training epochs: 2000; number of training images: 5) was indeed comparable with the accuracy in a recent 3D CNN-based, state-of-the-art algorithm 32 . The segmentation accuracy of the 3D CNN was quantified as Jaccard 0.92, Dice 0.96, and conformity 0.91 (semantic segmentation; ATUM/SEM data), whereas that of our standard 2D CNN was quantified as Jaccard 0.91, Dice 0.95, conformity 0.90 (semantic segmentation).…”
Recently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-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, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.
“…The segmentation accuracy was sufficiently high without any proofreading (Fig. 2C, bottom and right panel; RAND score: 0.85; see Methods), as expected from published results on 2D CNN-based segmentation 31,32 . The detailed instructions for the mitochondria segmentation task can be found at the public repository GitHub (see Data availability).Figure 3Performance survey in 2D CNN-based segmentation of neurons, synapses, and mitochondria.…”
Section: Resultssupporting
confidence: 82%
“…3D; ~0.3 RAND score). The accuracy of mitochondria segmentation in a standard CNN (network topology: ResNet; loss function: least square; number of layers: 9; training epochs: 2000; number of training images: 5) was indeed comparable with the accuracy in a recent 3D CNN-based, state-of-the-art algorithm 32 . The segmentation accuracy of the 3D CNN was quantified as Jaccard 0.92, Dice 0.96, and conformity 0.91 (semantic segmentation; ATUM/SEM data), whereas that of our standard 2D CNN was quantified as Jaccard 0.91, Dice 0.95, conformity 0.90 (semantic segmentation).…”
Recently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-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, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.
“…The test users successfully conducted the above procedure within the time indicated in parentheses, and the segmentation accuracy was high ( Fig. 2C, 5; RAND score: 0.85; see Methods), as expected from published results of CNN-based segmentation 38,39 . The detailed instruction for the mitochondria segmentation task can be found at https://github.com/urakubo/UNI-EM.…”
Section: Proofreading Annotation and Visualization The Test Userssupporting
confidence: 76%
“…Their detection and quantification are important for treating neuronal diseases 37 . Importantly, 2D CNNs yield sufficient segmentation accuracies 38,39 .…”
Section: Case 1: Mitochondria Segmentation Using 2d Cnnmentioning
Recently, there has been a rapid expansion in the field of micro-connectomics, which targets the threedimensional (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-DNNbased 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-theart 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.
“…Nevertheless, the complex textures as well as similar ultrastructures in EM images have made the segmentation of mitochondria a challenging problem. Recently, variety of methods have been developed to automatically detect and segment mitochondria (Liu et al, 2018 ; Xiao et al, 2018 ; Xie et al, 2018b ). GentleBoost classifier was trained for detecting mitochondria based on textural features (Vitaladevuni et al, 2008 ).…”
Together, mitochondria and the endoplasmic reticulum (ER) occupy more than 20% of a cell's volume, and morphological abnormality may lead to cellular function disorders. With the rapid development of large-scale electron microscopy (EM), manual contouring and three-dimensional (3D) reconstruction of these organelles has previously been accomplished in biological studies. However, manual segmentation of mitochondria and ER from EM images is time consuming and thus unable to meet the demands of large data analysis. Here, we propose an automated pipeline for mitochondrial and ER reconstruction, including the mitochondrial and ER contact sites (MAMs). We propose a novel recurrent neural network to detect and segment mitochondria and a fully residual convolutional network to reconstruct the ER. Based on the sparse distribution of synapses, we use mitochondrial context information to rectify the local misleading results and obtain 3D mitochondrial reconstructions. The experimental results demonstrate that the proposed method achieves state-of-the-art performance.
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