A Fiji plugin for object detection and tracking based on Mask RCNN.
It allows object detection, segmentation and tracking using a pre-trained model with the associated maskflow Python package.
IMPORTANT: This plugin is a proof of concept and in a very early stage. At the moment only a simple model to detect microtubules is provided. I hope to be able to continue the development at some point.
- Start Fiji.
- Click on
Help ▶ Update.... - In the new window, click on
Manage update sites. - Scroll to find
Maskflowin the columnName. Click on it. - Click
Closeand thenApply changes. - Restart Fiji.
- Open your image.
- Run the commands at
Plugins ► Maskflow.
Here is an example script:
# @Dataset data
# @CommandService cs
# @ModuleService ms
from sc.fiji.maskflow import ObjectDetector
inputs = {"model": None,
"modelName": "Microtubule",
"dataset": data,
"fillROIManager": True}}
module = ms.waitFor(cs.run(ObjectDetector, True, inputs))
table = module.getOutput("table")
masks = module.getOutput("masks")The plugin also comes with an object tracker based on the centroid of the detected masks:
# @Dataset data
# @CommandService cs
# @ModuleService ms
from sc.fiji.maskflow import ObjectDetector
from sc.fiji.maskflow import ObjectTracker
inputs = {"model": None,
"modelName": "Microtubule",
"dataset": data,
"fillROIManager": True}
module = ms.waitFor(cs.run(ObjectDetector, True, inputs))
table = module.getOutput("table")
masks = module.getOutput("masks")
inputs = {"masks": masks,
"table": table,
"linkingMaxDistance": 10,
"gapClosingMaxDistance": 10,
"maxFrameGap": 5,
"fillROIManager": True}
module = ms.waitFor(cs.run(ObjectTracker, True, inputs))
table = module.getOutput("resultTable")There is also a command that combine both detection and tracking:
# @Dataset data
# @CommandService cs
# @ModuleService ms
from sc.fiji.maskflow import ObjectDetectAndTrack
inputs = {"model": None,
"modelName": "Microtubule",
"dataset": data,
"linkingMaxDistance": 10,
"gapClosingMaxDistance": 10,
"maxFrameGap": 5,
"fillROIManager": True}
module = ms.waitFor(cs.run(ObjectDetectAndTrack, True, inputs))
table = module.getOutput("resultsTable")
masks = module.getOutput("masks")| Objects | Version | Description | Image Size | URL |
|---|---|---|---|---|
| Microtubule | 0.1 | WIP | 1280x1280 | https://storage.googleapis.com/nn-models/microtubule-v0.1.zip |
This type of neural networks are much more faster on GPU than CPU. To enable GPU support you need to manually replace libtensorflow_jni.jar to libtensorflow_jni_gpu.jar in your Fiji jars/ folder.
maskflow-fiji has been created by Hadrien Mary.
MIT. See LICENSE.txt
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