![]() ![]() Naito, T., Nagashima, Y., Taira, K., Uchio, N., Tsuji, S., Shimizu, J.: Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model. Motta, A., et al.: Dense connectomic reconstruction in layer 4 of the somatosensory cortex. More, H.L., Chen, J., Gibson, E., Donelan, J.M., Beg, M.F.: A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images. Mesbah, R., McCane, B., Mills, S.: Deep convolutional encoder-decoder for myelin and axon segmentation. Meirovitch, Y., et al.: A multi-pass approach to large-scale connectomics. Meirovitch, Y., Mi, L., Saribekyan, H., Matveev, A., Rolnick, D., Shavit, N.: Cross-classification clustering: an efficient multi-object tracking technique for 3-d instance segmentation in connectomics. Matejek, B., Haehn, D., Zhu, H., Wei, D., Parag, T., Pfister, H.: Biologically-constrained graphs for global connectomics reconstruction. Lee, K., Zung, J., Li, P., Jain, V., Seung, H.S.: Superhuman accuracy on the snemi3d connectomics challenge. Lee, K., Turner, N., Macrina, T., Wu, J., Lu, R., Seung, H.S.: Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy. Kornfeld, J., Denk, W.: Progress and remaining challenges in high-throughput volume electron microscopy. Januszewski, M., et al.: High-precision automated reconstruction of neurons with flood-filling networks. Helmstaedter, M.: Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Gonda, F., Wei, D., Pfister, H.: Consistent recurrent neural networks for 3d neuron segmentation. BioRxiv (2019)įunke, J., et al.: Large scale image segmentation with structured loss based deep learning for connectome reconstruction. Methods 14, 435–442 (2017)ĭorkenwald, S., et al.: Binary and analog variation of synapses between cortical pyramidal neurons. bioRxiv (2020)ĭorkenwald, S., et al.: Automated synaptic connectivity inference for volume electron microscopy. In: Medical Imaging (1999)ĭorkenwald, S., McKellar, C., et al.: Flywire: online community for whole-brain connectomics. In: NeurIPS (2012)Ĭuisenaire, O., Romero, E., Veraart, C., Macq, B.M.: Automatic segmentation and measurement of axons in microscopic images. 12, 88 (2018)Ĭiresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. Methods 14, 101–102 (2017)īerger, D.R., Seung, H.S., Lichtman, J.W.: Vast (volume annotation and segmentation tool): efficient manual and semi-automatic labeling of large 3D image stacks. 5, 4145–4161 (2014)īeier, T., et al.: Multicut brings automated neurite segmentation closer to human performance. Cell 182, 1372–1376 (2020)īégin, S., Dupont-Therrien, O., Bélanger, E., Daradich, A., Laffray, S., et al.: Automated method for the segmentation and morphometry of nerve fibers in large-scale cars images of spinal cord tissue. Ībbott, L.F., et al.: The mind of a mouse. SNEMI3D EM segmentation challenge and dataset. We publicly release our code and data at to foster the development of advanced methods. With this, we reproduce two published state-of-the-art methods and provide their evaluation results as a baseline. In addition, we densely annotate nine ground truth subvolumes for training, per each data volume. We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation, enabling large-scale evaluation of axon reconstruction methods. To address this, we introduce the AxonEM dataset, which consists of two \(30\times 30\times 30~\mu \)m \(^3\) EM image volumes from the human and mouse cortex, respectively. Worse still, there is no publicly available large-scale EM dataset from the cortex that provides dense ground truth segmentation for axons, making it difficult to develop and evaluate large-scale axon reconstruction methods. However, due to the complex morphology, an accurate reconstruction of cortical axons has become a major challenge. Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |