ghas-results/max-nucleus-segmenter

Identify nuclei in a microscopy image and assign each pixel of the image to a particular nucleus

IBM Developer Model Asset Exchange: Nucleus Segmenter

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.

Model Metadata

Domain	Application	Industry	Framework	Training Data	Input Data Format
Vision	Medical Image Segmentation	Health care	TensorFlow	2018 Data Science Bowl	Image(RGB)

References

• 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

Licenses

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

Training dataset

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.

Pre-requisites:

• 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.

Steps

1. Deploy from Quay
2. Deploy on Kubernetes
3. Run Locally

Deploy from Quay

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.

Deploy on Kubernetes

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.

Run Locally

1. Build the Model
2. Deploy the Model
3. Use the Model
4. Run the Notebook
5. Development
6. Cleanup

1. Build the Model

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).

2. Deploy the Model

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 max-nucleus-segmenter

3. Use the Model

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
    },

4. Run the Notebook

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.

5. Development

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).

6. Cleanup

To stop the Docker container, type CTRL + C in your terminal.

Resources and Contributions

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.