Electron Microscopy Image Segmentation
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Benchmarks
Most implemented
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images
DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images
Dense cellular segmentation for EM using 2D–3D neural network ensembles
Papers
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 Learning