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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.
Moment kernels: a simple and scalable approach for equivariance to rotations and reflections in deep convolutional networks
Unsupervised cell segmentation by fast Gaussian Processes
Reinforced Correlation Between Vision and Language for Precise Medical AI Assistant
Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization
CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification
Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging
COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification
CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification
Geometric Framework for Cell Oversegmentation
Single cell resolution 3D imaging and segmentation within intact live tissues
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
Progressive Vision-Language Prompt for Multi-Organ Multi-Class Cell Semantic Segmentation with Single Branch
CellSeg1: Robust Cell Segmentation with One Training Image
Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation
Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging
Cellpose+, a morphological analysis tool for feature extraction of stained cell images
Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images
Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis
MorphoSeg: An Uncertainty-Aware Deep Learning Method for Biomedical Segmentation of Complex Cellular Morphologies
TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation
Open Source Infrastructure for Automatic Cell Segmentation
InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation
NuLite -- Lightweight and Fast Model for Nuclei Instance Segmentation and Classification
Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion
LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels
Enhancing Cell Instance Segmentation in Scanning Electron Microscopy Images via a Deep Contour Closing Operator
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Medical Image Segmentation Using Directional Window Attention
An expert-driven data generation pipeline for histological images
Deep asymmetric mixture model for unsupervised cell segmentation
Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging
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