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Cell Segmentation
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Cell Segmentation is a task of splitting a microscopic image domain into segments, which represent individual instances of cells. It is a fundamental step in many biomedical studies, and it is regarded as a cornerstone of image-based cellular research. Cellular morphology is an indicator of a physiological state of the cell, and a well-segmented image can capture biologically relevant morphological information.
Source: Cell Segmentation by Combining Marker-controlled Watershed and Deep Learning
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy
Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining
Pushing the limits of cell segmentation models for imaging mass cytometry
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells
Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Morphological Profiling for Drug Discovery in the Era of Deep Learning
CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations
Guided Prompting in SAM for Weakly Supervised Cell Segmentation in Histopathological Images
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain
A Foundation Model for Cell Segmentation
Defining the boundaries: challenges and advances in identifying cells in microscopy images
DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Semi-supervised Instance Segmentation with a Learned Shape Prior
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
Towards Early Prediction of Human iPSC Reprogramming Success
PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies
Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing
An Instance Segmentation Dataset of Yeast Cells in Microstructures
Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation
SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration of the Brain
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