Skip to content

CenterSAM: Fully Automatic Prompt for Dense Nucleus Segmentation

License

Notifications You must be signed in to change notification settings

Rand2AI/CenterSAM

Repository files navigation

Introduction

This is the official github of the paper: CenterSAM: Fully Automatic Prompt for Dense Nucleus Segmentation.

The paper has been accepted by ISBI 2024 (International Symposium on Biomedical Imaging 2024), doi TBD.

CenterSAM is a fully automatic prompting segmentation approach which enabling accurate and generalizable nucleus segmentation for biomedical images. The Figure1 shows the overall archtecture of CenterSAM.

Figure1: Architecture of CenterSAM

Evaluation

The method have been evaluated on three different sized medical image datasets:

  • 2018 Data Science Bowl - Represents for "small" dataset

    • Number of Images: 51
    • Annotations: 32,217
  • MoNuSeg - Represents for "medium" dataset

    • Number of Images: 670
    • Annotations: 29,461
  • TissueNet - Represents for "large" dataset

    • Number of Images: 6990
    • Annotations: ~1.2 Million

Below Table shows quantitative results of comparison against State-Of-The-Art (SOTA) methods. The best results are highlighted in bold

2018 Data Science Bowl
Method DSC(%)↑ mIoU(%)↑
UNet++ 91.10 83.70
Deeplabv3+ [paper] [github] 88.80 83.70
SSFormer-S [paper] [github] 92.50 86.50
DuAT [paper] [github] 92.60 87.00
CenterSAM 92.20 86.60
MoNuSeg
Method DSC(%)↑ AJI(%)↑
UNet 74.56 60.22
UNet++ [paper] [github] 80.33 67.30
MAE [paper] [github] 73.68 58.62
MDM [paper] 81.01 68.25
CenterSAM 81.95 68.75
TissueNet
Method DSC(%)↑ SEG(%)↑
Detectron2 [github] 75.50 78.00
Cellulus [paper] [github] 64.10 52.40
StarDist [paper] [github] 59.40 38.20
Mesmer [paper] [github] 83.40 77.20
CenterSAM 88.70 79.50

Visualization

Tutorial

About

CenterSAM: Fully Automatic Prompt for Dense Nucleus Segmentation

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published