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Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as DeepLIIF, we present a single-step solution to stain deconvolution/separation, cell segmentation, and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunofluorescence (mpIF) staining of the same slides, we segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. Moreover, a new nuclear-envelop stain, LAP2beta, with high (>95%) cell coverage is introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. By simultaneously translating input IHC images to clean/separated mpIF channels and performing cell segmentation/classification, we show that our model trained on clean IHC Ki67 data can generalize to more noisy and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10, and TP53. We thoroughly evaluate our method on publicly available benchmark datasets as well as against pathologists' semi-quantitative scoring.

© This code is made available for non-commercial academic purposes.

Figure 1. Overview of DeepLIIF pipeline and sample input IHCs (different brown/DAB markers -- BCL2, BCL6, CD10, CD3/CD8, Ki67) with corresponding DeepLIIF-generated hematoxylin/mpIF modalities and classified (positive (red) and negative (blue) cell) segmentation masks. (a) Overview of DeepLIIF. Given an IHC input, our multitask deep learning framework simultaneously infers corresponding Hematoxylin channel, mpIF DAPI, mpIF protein expression (Ki67, CD3, CD8, etc.), and the positive/negative protein cell segmentation, baking explainability and interpretability into the model itself rather than relying on coarse activation/attention maps. In the segmentation mask, the red cells denote cells with positive protein expression (brown/DAB cells in the input IHC), whereas blue cells represent negative cells (blue cells in the input IHC). (b) Example DeepLIIF-generated hematoxylin/mpIF modalities and segmentation masks for different IHC markers. DeepLIIF, trained on clean IHC Ki67 nuclear marker images, can generalize to noisier as well as other IHC nuclear/cytoplasmic marker images.

1. Python 3.8
2. Docker

DeepLIIF can be pip installed:

$ python3.8 -m venv venv
$ source venv/bin/activate
(venv) $ pip install git+https://github.com/nadeemlab/DeepLIIF.git

The package is composed of two parts:

1. A library that implements the core functions used to train and test DeepLIIF models.
2. A CLI to run common batch operations including training, batch testing and Torchscipt models serialization.

You can list all available commands:

(venv) $ deepliif --help
Usage: deepliif [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  prepare-testing-data   Preparing data for testing
  prepare-training-data  Preparing data for training
  serialize              Serialize DeepLIIF models using Torchscript
  test                   Test trained models
  train                  General-purpose training script for multi-task...

For training, all image sets must be 512x512 and combined together in 3072x512 images (six images of size 512x512 stitched together horizontally). The data need to be arranged in the following order:

XXX_Dataset 
    ├── train
    └── val

We have provided a simple function in the CLI for preparing data for training.

• To prepare data for training, you need to have the image dataset for each image (including IHC, Hematoxylin Channel, mpIF DAPI, mpIF Lap2, mpIF marker, and segmentation mask) in the input directory. Each of the six images for a single image set must have the same naming format, with only the name of the label for the type of image differing between them. The label names must be, respectively: IHC, Hematoxylin, DAPI, Lap2, Marker, Seg. The command takes the address of the directory containing image set data and the address of the output dataset directory. It first creates the train and validation directories inside the given output dataset directory. It then reads all of the images in the input directory and saves the combined image in the train or validation directory, based on the given validation_ratio.

deepliif prepare-training-data --input-dir /path/to/input/images
                               --output-dir /path/to/output/images
                               --validation-ratio 0.2

To train a model:

deepliif train --dataroot /path/to/input/images 
                --name Model_Name 

or

python train.py --dataroot /path/to/input/images 
                --name Model_Name 

• To view training losses and results, open the URL https://localhost:8097. For cloud servers replace localhost with your IP.
• Epoch-wise intermediate training results are in DeepLIIF/checkpoints/Model_Name/web/index.html.
• Trained models will be by default be saved in DeepLIIF/checkpoints/Model_Name.
• Training datasets can be downloaded here.

DP: To train a model you can use DP. DP is single-process. It means that all the GPUs you want to use must be on the same machine so that they can be included in the same process - you cannot distribute the training across multiple GPU machines, unless you write your own code to handle inter-node (node = machine) communication. To split and manage the workload for multiple GPUs within the same process, DP uses multi-threading. You can find more information on DP here.

To train a model with DP (Example with 2 GPUs (on 1 machine)):

deepliif train --dataroot <data_dir> --batch-size 6 --gpu-ids 0 --gpu-ids 1

Note that batch-size is defined per process. Since DP is a single-process method, the batch-size you set is the effective batch size.

DDP: To train a model you can use DDP. DDP usually spawns multiple processes. DeepLIIF's code follows the PyTorch recommendation to spawn 1 process per GPU (doc). If you want to assign multiple GPUs to each process, you will need to make modifications to DeepLIIF's code (see doc). Despite all the benefits of DDP, one drawback is the extra GPU memory needed for dedicated CUDA buffer for communication. See a short discussion here. In the context of DeepLIIF, this means that there might be situations where you could use a bigger batch size with DP as compared to DDP, which may actually train faster than using DDP with a smaller batch size. You can find more information on DDP here.

To launch training using DDP on a local machine, use deepliif trainlaunch. Example with 2 GPUs (on 1 machine):

deepliif trainlaunch --dataroot <data_dir> --batch-size 3 --gpu-ids 0 --gpu-ids 1 --use-torchrun "--nproc_per_node 2"

Note that

1. batch-size is defined per process. Since DDP is a single-process method, the batch-size you set is the batch size for each process, and the effective batch size will be batch-size multiplied by the number of processes you started. In the above example, it will be 3 * 2 = 6.
2. You still need to provide all GPU ids to use to the training command. Internally, in each process DeepLIIF picks the device using gpu_ids[local_rank]. If you provide --gpu-ids 2 --gpu-ids 3, the process with local rank 0 will use gpu id 2 and that with local rank 1 will use gpu id 3.
3. -t 3 --log_dir <log_dir> is not required, but is a useful setting in torchrun that saves the log from each process to your target log directory. For example:

deepliif trainlaunch --dataroot <data_dir> --batch-size 3 --gpu-ids 0 --gpu-ids 1 --use-torchrun "-t 3 --log_dir <log_dir> --nproc_per_node 2"

4. If your PyTorch is older than 1.10, DeepLIIF calls torch.distributed.launch in the backend. Otherwise, DeepLIIF calls torchrun.

The installed deepliif uses Dask to perform inference on the input IHC images. Before running the test command, the model files must be serialized using Torchscript. To serialize the model files:

deepliif serialize --models-dir /path/to/input/model/files
                   --output-dir /path/to/output/model/files

• By default, the model files are expected to be located in DeepLIIF/model-server/DeepLIIF_Latest_Model.
• By default, the serialized files will be saved to the same directory as the input model files.

To test the model:

deepliif test --input-dir /path/to/input/images 
              --output-dir /path/to/output/images 
              --tile-size 512

or

python test.py --dataroot /path/to/input/images 
               --name Model_Name  

• The latest version of the pretrained models can be downloaded here.
• Before running test on images, the model files must be serialized as described above.
• The serialized model files are expected to be located in DeepLIIF/model-server/DeepLIIF_Latest_Model.
• The test results will be saved to the specified output directory, which defaults to the input directory.
• The default tile size is 512.
• Testing datasets can be downloaded here.

If you prefer, it is possible to run the model using Torchserve. Please see below for instructions on how to deploy the model with Torchserve and for an example of how to run the inference.

We provide a Dockerfile that can be used to run the DeepLIIF models inside a container. First, you need to install the Docker Engine. After installing the Docker, you need to follow these steps:

• Download the pretrained model and place them in DeepLIIF/checkpoints/DeepLIIF_Latest_Model.
• Change XXX of the WORKDIR line in the DockerFile to the directory containing the DeepLIIF project.
• To create a docker image from the docker file:

docker build -t cuda/deepliif .

The image is then used as a base. You can copy and use it to run an application. The application needs an isolated environment in which to run, referred to as a container.

• To create and run a container:

 docker run -it -v `pwd`:`pwd` -w `pwd` cuda/deepliif deepliif test --input-dir Sample_Large_Tissues

When you run a container from the image, the deepliif CLI will be available. You can easily run any CLI command in the activated environment and copy the results from the docker container to the host.

If you don't have access to GPU or appropriate hardware, we have also created Google CoLab project for your convenience. Please follow the steps in the provided notebook to install the requirements and run the training and testing scripts. All the libraries and pretrained models have already been set up there. The user can directly run DeepLIIF on their images using the instructions given in the Google CoLab project.

If you don't have access to GPU or appropriate hardware and just want to use ImageJ to run inference, we have also created an ImageJ plugin for your convenience.

If you don't have access to GPU or appropriate hardware and don't want to install ImageJ, we have also created a cloud-native DeepLIIF deployment with a user-friendly interface to upload images, visualize, interact, and download the final results.

The first version of DeepLIIF model suffered from its inability to separate IHC positive cells in some large clusters, resulting from the absence of clustered positive cells in our training data. To infuse more information about the clustered positive cells into our model, we present a novel approach for the synthetic generation of IHC images using co-registered data. We design a GAN-based model that receives the Hematoxylin channel, the mpIF DAPI image, and the segmentation mask and generates the corresponding IHC image. The model converts the Hematoxylin channel to gray-scale to infer more helpful information such as the texture and discard unnecessary information such as color. The Hematoxylin image guides the network to synthesize the background of the IHC image by preserving the shape and texture of the cells and artifacts in the background. The DAPI image assists the network in identifying the location, shape, and texture of the cells to better isolate the cells from the background. The segmentation mask helps the network specify the color of cells based on the type of the cell (positive cell: a brown hue, negative: a blue hue).

In the next step, we generate synthetic IHC images with more clustered positive cells. To do so, we change the segmentation mask by choosing a percentage of random negative cells in the segmentation mask (called as Neg-to-Pos) and converting them into positive cells. Some samples of the synthesized IHC images along with the original IHC image are shown in Figure 2.

Figure 2. Overview of synthetic IHC image generation. (a) A training sample of the IHC-generator model. (b) Some samples of synthesized IHC images using the trained IHC-Generator model. The Neg-to-Pos shows the percentage of the negative cells in the segmentation mask converted to positive cells.

We created a new dataset using the original IHC images and synthetic IHC images. We synthesize each image in the dataset two times by setting the Neg-to-Pos parameter to %50 and %70. We re-trained our network with the new dataset. You can find the new trained model here.

To register the de novo stained mpIF and IHC images, you can use the registration framework in the 'Registration' directory. Please refer to the README file provided in the same directory for more details.

To train DeepLIIF, we used a dataset of lung and bladder tissues containing IHC, hematoxylin, mpIF DAPI, mpIF Lap2, and mpIF Ki67 of the same tissue scanned using ZEISS Axioscan. These images were scaled and co-registered with the fixed IHC images using affine transformations, resulting in 1667 co-registered sets of IHC and corresponding multiplex images of size 512x512. We randomly selected 709 sets for training, 358 sets for validation, and 600 sets for testing the model. We also randomly selected and segmented 41 images of size 640x640 from recently released BCDataset which contains Ki67 stained sections of breast carcinoma with Ki67+ and Ki67- cell centroid annotations (for cell detection rather than cell instance segmentation task). We split these tiles into 164 images of size 512x512; the test set varies widely in the density of tumor cells and the Ki67 index. You can find this dataset here.

We are also creating a self-configurable version of DeepLIIF which will take as input any co-registered H&E/IHC and multiplex images and produce the optimal output. If you are generating or have generated H&E/IHC and multiplex staining for the same slide (de novo staining) and would like to contribute that data for DeepLIIF, we can perform co-registration, whole-cell multiplex segmentation via ImPartial, train the DeepLIIF model and release back to the community with full credit to the contributors.

This section describes how to run DeepLIIF's inference using Torchserve workflows. Workflows con be composed by both PyTorch models and Python functions that can be connected through a DAG. For DeepLIIF there are 4 main stages (see Figure 3):

• Pre-process deserialize the image from the request and return a tensor created from it.
• G1-4 run the ResNets to generate the Hematoxylin, DAPI, LAP2 and Ki67 masks.
• G51-5 run the UNets and apply Weighted Average to generate the Segmentation image.
• Aggregate aggregate and serialize the results and return to user.

Figure 3. Composition of DeepLIIF nets into a Torchserve workflow

In practice, users need to call this workflow for each tile generated from the original image.
A common use case scenario would be:

1. Load an IHC image and generate the tiles.
2. For each tile
   1. Resize to 512x512 and transform to tensor.
   2. Serialize the tensor and use the inference API to generate all the masks
   3. Deserialize the results
3. Stitch back the results and apply post-processing operations

The next sections show how to deploy the model server.

1. Install Torchserve and torch-model-archiver following these instructions. In MacOS, navigate to the model-server directory:

cd model-server
python3 -m venv venv
source venv/bin/activate
pip install torch torchserve torch-model-archiver torch-workflow-archiver 

2. Download and unzip the latest version of the DeepLIIF models from zenodo.

wget https://zenodo.org/record/4751737/files/DeepLIIF_Latest_Model.zip
unzip DeepLIIF_Latest_Model.zip

In order to run the DeepLIIF nets using Torchserve, they first need to be archived as MAR files. In this section we will create the model artifacts and archive them in the model store. First, inside model-server create a directory to store the models.

mkdir model-store

For every ResNet (G1, G2, G3, G4) run replacing the name of the net:

torch-model-archiver --force --model-name <Gx> \
    --model-file resnet.py \
    --serialized-file ./DeepLIIF_Latest_Model/latest_net_<Gx>.pth \
    --export-path model-store \
    --handler net_handler.py \
    --requirements-file model_requirements.txt

and for the UNets (G51, G52, G53, G54, G54) switch the model file from resnet.py to unet.py:

torch-model-archiver --force --model-name <G5x> \
    --model-file unet.py \
    --serialized-file ./DeepLIIF_Latest_Model/latest_net_<G5x>.pth \
    --export-path model-store \
    --handler net_handler.py \
    --requirements-file model_requirements.txt

Once all the models have been packaged and made available in the model store, they can be composed into a workflow archive. Finally, create the archive for the workflow represented in Figure 3.

torch-workflow-archiver -f --workflow-name deepliif \
    --spec-file deepliif_workflow.yaml \
    --handler deepliif_workflow_handler.py \
    --export-path model-store

Once all artifacts are available in the model store, run the model server.

torchserve --start --ncs \
    --model-store model-store \
    --workflow-store model-store  \
    --ts-config config.properties

An additional step is needed to register the deepliif workflow on the server.

curl -X POST "https://127.0.0.1:8081/workflows?url=deepliif.war"

The snippet below shows an example of how to cosume the Torchserve workflow API using Python.

import base64
import requests
from io import BytesIO

import torch

from deepliif.preprocessing import transform

def deserialize_tensor(bs):
    return torch.load(BytesIO(base64.b64decode(bs.encode())))

def serialize_tensor(ts):
    buffer = BytesIO()
    torch.save(ts, buffer)
    return base64.b64encode(buffer.getvalue()).decode('utf-8')

TORCHSERVE_HOST = 'https://127.0.0.1:8080'

img = load_tile()

ts = transform(img.resize((512, 512)))

res = requests.post(
    f'{TORCHSERVE_HOST}/wfpredict/deepliif',
    json={'img': serialize_tensor(ts)}
)

res.raise_for_status()

masks = {k: deserialize_tensor(v) for k, v in res.json().items()}

Please report all issues on the public forum.

© Nadeem Lab - DeepLIIF code is distributed under Apache 2.0 with Commons Clause license, and is available for non-commercial academic purposes.

• This code is inspired by CycleGAN and pix2pix in PyTorch.

If you find our work useful in your research or if you use parts of this code, please cite our paper:

@article{ghahremani2022deepliif,
  title={Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification},
  author={Ghahremani, Parmida and Li, Yanyun and Kaufman, Arie and Vanguri, Rami and Greenwald, Noah and Angelo, Michael and Hollmann, Travis J and Nadeem, Saad},
  journal={Nature Machine Intelligence},
  year={2022}
}

@article{ghahremani2022deepliifui,
  title={DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides},
  author={Ghahremani, Parmida and Marino, Joseph and Dodds, Ricardo and Nadeem, Saad},
  journal={CVPR},
  year={2022}
}