Papers Electron Microscopy Image Segmentation
“Electron Microscopy Image Segmentation” 태그가 달린 논문 17편 · 필터 해제
Are Vision Foundation Models Foundational for Electron Microscopy Image Segmentation?
Although vision foundation models (VFMs) are increasingly reused for biomedical image analysis, it remains unclear whether the latent representations they provide are general enough to support effective transfer and reus…
Electron Microscopy Image Segmentationparameter-efficient fine-tuningAttention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images
Time-lapse microscopy imaging is a crucial technique in biomedical studies for observing cellular behavior over time, providing essential data on cell numbers, sizes, shapes, and interactions. Manual analysis of hundreds…
Cell SegmentationElectron Microscopy Image SegmentationImage SegmentationInstance Segmentation+2DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images
The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for biomedical research and clinical diagnostics. However, accurately annotating de…
Electron Microscopy Image SegmentationImage RegistrationImage SegmentationMedical Image Segmentation+3MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Cell segmentation is a fundamental task for computational biology analysis. Identifying the cell instances is often the first step in various downstream biomedical studies. However, many cell segmentation algorithms, inc…
Cell SegmentationElectron Microscopy Image SegmentationInstance SegmentationSegmentation+1Dense cellular segmentation for EM using 2D–3D neural network ensembles
Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in ima…
3D Semantic SegmentationElectron Microscopy Image SegmentationSegmentationSemantic SegmentationCEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning
Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to un…
Electron Microscopy Image SegmentationSegmentationTransfer LearningUnderstanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly l…
Deep LearningElectron Microscopy Image SegmentationImage SegmentationSemantic SegmentationDense Transformer Networks for Brain Electron Microscopy Image Segmentation
The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are…
DecoderElectron Microscopy Image SegmentationImage SegmentationSemantic SegmentationTeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborizati…
Electron Microscopy Image SegmentationImage ReconstructionBiologically-Constrained Graphs for Global Connectomics Reconstruction
Most current state-of-the-art connectome reconstruction pipelines have two major steps: initial pixel-based segmentation with affinity prediction and watershed transform, and refined segmentation by merging over-segmente…
Electron Microscopy Image Segmentationgraph constructiongraph partitioningSegmentationUNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images
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) i…
3D ReconstructionElectron Microscopy Image SegmentationImage SegmentationSegmentation+1Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images
In the deep metric learning approach to image segmentation, a convolutional net densely generates feature vectors at the pixels of an image. Pairs of feature vectors are trained to be similar or different, depending on w…
Electron Microscopy Image SegmentationImage SegmentationInstance SegmentationMetric Learning+1High-Precision Automated Reconstruction of Neurons with Flood-filling Networks
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without cos…
Electron Microscopy Image SegmentationImage ReconstructionVocal Bursts Intensity PredictionSuperhuman Accuracy on the SNEMI3D Connectomics Challenge
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images. Recent years have seen great improvements in accuracy, as evidenced by submissions …
3D ReconstructionElectron Microscopy Image SegmentationSSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determine region …
Electron Microscopy Image SegmentationImage SegmentationSegmentationSemantic SegmentationLarge-Scale Electron Microscopy Image Segmentation in Spark
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 nanom…
Electron Microscopy Image SegmentationImage SegmentationSegmentationSemantic SegmentationAn Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation
To build the connectomics map of the brain, we developed a new algorithm that can automatically refine the Membrane Detection Probability Maps (MDPM) generated to perform automatic segmentation of electron microscopy (EM…
BIG-bench Machine LearningElectron Microscopy Image SegmentationImage SegmentationSegmentation+1