https://github.com/buswinka/bism

Biomedical Image Segmentation Models
https://github.com/buswinka/bism

Last synced: 2 months ago
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Biomedical Image Segmentation Models

• Host: GitHub
• URL: https://github.com/buswinka/bism
• Owner: buswinka
• License: mit
• Created: 2022-09-13T17:41:55.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-01-16T18:38:24.000Z (almost 1 year ago)
• Last Synced: 2024-11-01T23:05:24.325Z (3 months ago)
• Language: Python
• Size: 311 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# bism - Biomedical Image Segmentation Models

BISM is a repository for training and evaluating biomedical instance segmentation models -- something akin to the `timm` package for 2D image tasks, but 3D instance segmentation. 

When at all possible, each model will offer a 2D or 3D implementation, however we will not provide pre-trained model files. 

No Documentation right now. In general, you launch a training run through a yaml configuration file. 

Check out `bism.train.__main__.py` as the starting point for training. `bism.config.config.py` for the default 

configuration for each approach. This should (hopefully) allow for repeatable training of 3D instance segmentation 

models of various types. 

To execute a training config, simply run `python bism/train --config_file "Path/To/Your/File.yaml"`.

To run a pretrained model, simply run `python bism/eval -m "path/to/model/file.trch" -i "path/to/image.tif"`

To launch the model inspector, run `python bism/gui`

This module is under active development so should not be used for anything but research purposes!

Current Models

---------------

| Model          | 2D  | 3D  | Scriptable |

|----------------|-----|-----|------------|

| UNet           | ✓   | ✓   | ✓          |

| UNeXT          | ✓   | ✓   | ✓          |

| Recurrent UNet | ✓   | ✓   | ✓          |

| Residual UNet  |     |     |            |

| Unet++         | ✓   | ✓   | ✓          |

| CellposeNet    | ✓   | ✓   | ✓          |

Current Generic Blocks

----------------------

| BLOCK NAME           | 2D   | 3D  |

|----------------------|------|-----|

| UNeXT Block          | ✓    | ✓   |

| ConcatConv           | ✓    | ✓   |

| Recurrent UNet BLock | ✓    | ✓   |

| Residual UNet BLock  | ✓    | ✓   |

| DropPath             | ✓    | ✓   |

| LayerNorm            | ✓    | ✓   |

| UpSample             | ✓    | ✓   |

| ViT Block            |      |     |

Segmentation Implementation

---------------------------

| APPROACH          | 2D | 3D |

|-------------------|----|----|

| Cellpose          |    |    |

| Affinities        |    | ✓  |

| Local Shape Desc. |    | ✓  |

| Omnipose          |    | ✓  |

| Auto Context LSD  |    | ✓  |

| Multitask LSDs    |    | ✓  |

| Semantic          | ✓  | ✓  |

| Mask RCNN         | ✓  |    |

Loss Functions

--------------

| Function         | Implemented |

|------------------|-------------|

| Dice             | ✓           |

| CL Dice          | ✓           |

| Tverksy          | ✓           |

| Jaccard          | ✓           |