SALEM2: Segment Anything in Light and Electron Microscopy via Membrane Guidance
This repository has been archived. Please instead refer SAEM2 in SAvEM3.
Quantitative comparison: SAM, HQ-SAM, Mobile-SAM (from HQ-SAM) and Micro-SAM, as well as trained SAM and HQ-SAM in both LM and EM datasets.
Qualitative comparison: CellPose in LM, ilastik (pretrained models from https://bioimage.io/#/?partner=ilastik) and Superhuman (onnx models from https://github.com/seung-lab/DeepEM/releases) in EM.
Raw |
GT |
CellPose |
OmniPose |
SALM2 |
Raw |
GT |
CellPose |
SALM2 |
Raw |
GT |
Superhuman (section 15) |
SAEM2 (section 15) |
Raw |
GT |
ilastik (section 103) |
SAEM2 (section 103) |
More results can be found in Google Drive: BBC039, NeurIPS22-CellSeg and CREMI. We used CellPose and OmniPose with the configurations of nuclei (flow_threshold=0.3)
for Bare Nuclei, tissuenet (flow_threshold=0.6)
for Weak Boundary, nuclei (flow_threshold=0.6)
and bact_phase_omni
for Elongated. We used the open-sourced trained models of ilastik and Superhuman with alternative preprocessing steps.
Thanks to the other authors and MiRA Team for their support and resources.
Thanks for their public code and released models.