SOTAVerified

Cell Segmentation

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

Papers

Showing 110 of 178 papers

TitleStatusHype
PixCell: A generative foundation model for digital histopathology images0
Moment kernels: a simple and scalable approach for equivariance to rotations and reflections in deep convolutional networks0
Unsupervised cell segmentation by fast Gaussian ProcessesCode0
Reinforced Correlation Between Vision and Language for Precise Medical AI Assistant0
Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization0
CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and ClassificationCode1
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging0
COIN: Confidence Score-Guided Distillation for Annotation-Free Cell SegmentationCode0
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and ClassificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Dual U-Net (Neighbor distances)SEG (~Mean IoU)0.9Unverified
2DecLSTMSEG (~Mean IoU)0.84Unverified
3EncLSTMSEG (~Mean IoU)0.81Unverified