中文
相关论文

相关论文: X-Ray2EM: Uncertainty-Aware Cross-Modality Image R…

200 篇论文

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer…

计算机视觉与模式识别 · 计算机科学 2016-09-28 Pascal Fua , Graham Knott

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing…

计算机视觉与模式识别 · 计算机科学 2012-10-03 Tao Hu , Juan Nunez-Iglesias , Shiv Vitaladevuni , Lou Scheffer , Shan Xu , Mehdi Bolorizadeh , Harald Hess , Richard Fetter , Dmitri Chklovskii

Mammalian whole-brain connectomes are a foundational ingredient for holistic understanding of brains. Indeed, imaging connectomes at sufficient resolution to densely reconstruct cellular morphology and synapses represents a longstanding…

神经元与认知 · 定量生物学 2025-02-03 Logan Thrasher Collins , Todd Huffman , Randal Koene

The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local…

计算机视觉与模式识别 · 计算机科学 2023-02-28 Tri Nguyen , Mukul Narwani , Mark Larson , Yicong Li , Shuhan Xie , Hanspeter Pfister , Donglai Wei , Nir Shavit , Lu Mi , Alexandra Pacureanu , Wei-Chung Lee , Aaron T. Kuan

The emerging field of connectomics aims to unlock the mysteries of the brain by understanding the connectivity between neurons. To map this connectivity, we acquire thousands of electron microscopy (EM) images with nanometer-scale…

定量方法 · 定量生物学 2016-04-04 Stephen M. Plaza , Stuart E. Berg

Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due to the complex morphology, an accurate reconstruction of…

Existing methods in nanoscale connectomics are at present too slow to map entire mammalian brains. As an emerging approach, expansion microscopy (ExM) has enormous promise, yet it still suffers from throughput limitations. Mapping the human…

定量方法 · 定量生物学 2026-01-21 Logan Thrasher Collins

Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM) have produced massive 3D image volumes of nanoscale brain tissue…

Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy. One of the main challenges in connectomics research is…

计算机视觉与模式识别 · 计算机科学 2021-04-14 Tran Minh Quan , David G. C. Hildebrand , Won-Ki Jeong

High resolution volumetric neuroimaging datasets from electron microscopy (EM) and x-ray micro and holographic-nano tomography (XRM/XHN) are being generated at an increasing rate and by a growing number of research teams. These datasets are…

The most established method of reconstructing neural circuits from animals involves slicing tissue very thin, then taking mosaics of electron microscope (EM) images. To trace neurons across different images and through different sections,…

定量方法 · 定量生物学 2013-04-23 Louis K. Scheffer , Bill Karsh , Shiv Vitaladevun

The promise of large-scale, high-resolution datasets from Electron Microscopy (EM) and X-ray Microtomography (XRM) lies in their ability to reveal neural structures and synaptic connectivity, which is critical for understanding the brain.…

Neuroimaging to neuropathology correlation (NTNC) promises to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical care. NTNC traditionally requires a volumetric MRI scan,…

Identifying complex neural circuitry from electron microscopic (EM) images may help unlock the mysteries of the brain. However, identifying this circuitry requires time-consuming, manual tracing (proofreading) due to the size and intricacy…

定量方法 · 定量生物学 2014-09-04 Stephen M. Plaza

Although deep encoder-decoder networks have achieved astonishing performance for mitochondria segmentation from electron microscopy (EM) images, they still produce coarse segmentations with lots of discontinuities and false positives.…

计算机视觉与模式识别 · 计算机科学 2020-01-30 Zhimin Yuan , Jiajin Yi , Zhengrong Luo , Zhongdao Jia , Jialin Peng

The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms. Although segmentation algorithms eliminate the necessity of tracing the neurons by hand, significant…

计算机视觉与模式识别 · 计算机科学 2015-09-29 Toufiq Parag

The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g.,…

Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have successfully demonstrated the use of convolutional neural networks…

计算机视觉与模式识别 · 计算机科学 2017-02-27 Shibani Santurkar , David Budden , Alexander Matveev , Heather Berlin , Hayk Saribekyan , Yaron Meirovitch , Nir Shavit

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics. An important computation for reconstruction is the detection of neuronal boundaries. Images…

计算机视觉与模式识别 · 计算机科学 2015-08-21 Kisuk Lee , Aleksandar Zlateski , Ashwin Vishwanathan , H. Sebastian Seung

The current neuron reconstruction pipeline for electron microscopy (EM) data usually includes automatic image segmentation followed by extensive human expert proofreading. In this work, we aim to reduce human workload by predicting…

计算机视觉与模式识别 · 计算机科学 2024-01-09 Qihua Chen , Xuejin Chen , Chenxuan Wang , Yixiong Liu , Zhiwei Xiong , Feng Wu
‹ 上一页 1 2 3 10 下一页 ›