The Nucleus Segmenter model detects nuclei in a microscopy image and specifies the pixels in the image that are assigned to each nucleus. The model is developed based on the architecture of Mask R-CNN using Feature Pyramid network (FPN) and a ResNet50 backbone. Given an image (of size 64 x 64, 128 x 128 or 256 x 256), this model outputs the segmentation masks and probabilities for each detected nucleus. The mask is compressed using Run-length encoding (RLE).
The model is based on the TF implementation of Mask R-CNN. The model is trained on the Broad Bioimage Benchmark Collection (Accession number BBBC038, Version 1) dataset of annotated biological images. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Developer Model Asset Exchange.
Domain | Application | Industry | Framework | Training Data | Input Data Format |
---|---|---|---|---|---|
Vision | Medical Image Segmentation | Health care | TensorFlow | 2018 Data Science Bowl | Image(RGB) |
- He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017, October. Mask R-CNN. In Computer Vision (ICCV), 2017 IEEE International Conference on (pp. 2980-2988). IEEE.
- Ljosa, V., Sokolnicki, K.L. and Carpenter, A.E., 2012. Annotated high-throughput microscopy image sets for validation. Nature methods, 9(7), pp.637-637.
- Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].
- Mask R-CNN Github Repository
Component | License | Link |
---|---|---|
This repository | Apache 2.0 | LICENSE |
Model Weights | Apache 2.0 | LICENSE |
Model Code (3rd party) | MIT | LICENSE |
Test samples | Various | Samples README |
We used image set BBBC038v1 from 2018 Data Science Bowl®, presented by Booz Allen Hamilton and Kaggle. The dataset is available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012]. According to this post, the dataset is under Creative Commons license 0 (CC0 public domain). Credits for the images are available here.
docker
: The Docker command-line interface. Follow the installation instructions for your system.- The minimum recommended resources for this model is 2GB Memory and 1 CPU.
To run the docker image, which automatically starts the model serving API, run:
$ docker run -it -p 5000:5000 quay.io/codait/max-nucleus-segmenter
This will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.
You can also deploy the model on Kubernetes using the latest docker image on Quay.
On your Kubernetes cluster, run the following commands:
$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Nucleus-Segmenter/master/max-nucleus-segmenter.yaml
The model will be available internally at port 5000
, but can also be accessed externally through the NodePort
.
Clone this repository locally. In a terminal, run the following command:
$ git clone https://github.com/IBM/MAX-Nucleus-Segmenter
Change directory into the repository base folder:
$ cd MAX-Nucleus-Segmenter
To build the docker image locally, run:
$ docker build -t max-nucleus-segmenter .
All required model assets will be downloaded during the build process. Note that currently this docker image is CPU only (we will add support for GPU images later).
To run the docker image, which automatically starts the model serving API, run:
$ docker run -it -p 5000:5000 max-nucleus-segmenter
The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000
to load
it. From there you can explore the API and also create test requests. Use the model/predict
endpoint to load a test
image (you can use one of the test images from the assets
folder) and get predicted probabilities and segmentation
masks for the image from the API.
You can also test it on the command line, for example:
$ curl -F "image=@samples/example.png" -XPOST http://localhost:5000/model/predict
You should see a JSON response like that below:
{
"status": "ok",
"predictions": [
{
"mask": [
3507,
1,
3571,
5,
3635,
6,
3700,
5,
3766,
4,
3831,
2
],
"probability": 0.9837305545806885
},
...
{
"mask": [
1079,
1,
1144,
1,
1207,
3,
1271,
4,
1336,
3,
1401,
3,
1465,
3,
1530,
3,
1595,
2
],
"probability": 0.9726951122283936
},
Once the model server is running, you can see how to use it by walking through the demo notebook. Note the demo requires jupyter
, numpy
, matplotlib
, scikit-image
, json
, and requests
.
Run the following command from the model repo base folder, in a new terminal window (leaving the model server running in the other terminal window):
$ jupyter notebook
This will start the notebook server. You can open the simple demo notebook by clicking on demo.ipynb
.
To run the Flask API app in debug mode, edit config.py
to set DEBUG = True
under the application settings. You will then need to rebuild the docker image (see step 1).
To stop the Docker container, type CTRL
+ C
in your terminal.
If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.