Table 2.
Project | Reference | Model architecture | Use cases | Advantages | Open-source code |
---|---|---|---|---|---|
U-Net | (76) | U-Net | Segmentation of various structures, very adaptive |
|
Various |
StarDist | (11) | Modified U-Net + polygonal representation of objects | 2D and 3D object detection and segmentation, designed for complex shapes. |
|
https://github.com/stardist/stardist |
Cellpose | (60) | Modified U-Net + vector representation of objects | 2D and 3D object detection and segmentation, continuously improved by the community training |
|
https://github.com/MouseLand/cellpose |
SplineDist | (77) | Modified U-Net (extended StarDist) | Extension of the StarDist network, segmentation of objects with more complex shapes and curves |
|
https://github.com/uhlmanngroup/splinedist |
EmbedSeg | (78) | ERFNet (convolutional network with residual connections) | Instance segmentation of cells and nuclei in microscopy |
|
https://github.com/juglab/EmbedSeg |
CellSeg | (79) | R-CNN (region based convolutional network) | Segmentation on highly multiplexed fluorescence images |
|
https://github.com/michaellee1/CellSeg |
Every model can be used for the task it was originally trained for or retrained from scratch, given enough training data is present. All of the listed models are freely and publicly available, some are even provided with hands on tutorials to help new researchers get started using them. While this list is not exhaustive, the authors would recommend to start out with one of the models listed above, due to their ease of use and already proven accuracy at the task of micrograph analysis.