Package: deepliif
Deep-Learning Inferred Multiplex Immunofluorescence for Immunohistochemical Image QuantificationNature MI'22|CVPR'22|MICCAI'23|Histopathology'23|Cloud Deployment|Documentation|SupportReporting 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. Trained on IHC, DeepLIIF generalizes well to H&E images for out-of-the-box nuclear
segmentation.DeepLIIFis deployed as a free publicly available cloud-native platform (https://deepliif.org) with Bioformats (more than 150 input formats supported) and MLOps pipeline. We also releaseDeepLIIFimplementations for single/multi-GPU training, Torchserve/Dask+Torchscript deployment, and auto-scaling via Pulumi (1000s of concurrent connections supported); details can be found in ourdocumentation.DeepLIIFcan be run locally (GPU required) bypip installing the packageand using the deepliif CLI command.DeepLIIFcan be used remotely (no GPU required) through thehttps://deepliif.orgwebsite, calling thecloud API via Python, or via theImageJ/Fiji plugin; details for the free cloud-native platform can be found in ourCVPR'22 paper.Â© This code is made available for non-commercial academic purposes.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.PrerequisitesPython 3.8DockerInstallingdeepliifDeepLIIF can bepipinstalled:$condacreate--namedeepliif_envpython=3.8
$condaactivatedeepliif_env(deepliif_env)$condainstall-cconda-forgeopenjdk(deepliif_env)$pipinstalldeepliifThe package is composed of two parts:A library that implements the core functions used to train and test DeepLIIF models.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
  serialize              Serialize DeepLIIF models using Torchscript
  test                   Test trained models
  train                  General-purpose training script for multi-task...Training DatasetFor 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 
    â”œâ”€â”€ train
    â””â”€â”€ valWe 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 givenvalidation_ratio.deepliif prepare-training-data --input-dir /path/to/input/images
                               --output-dir /path/to/output/images
                               --validation-ratio 0.2TrainingTo train a model:deepliif train --dataroot /path/to/input/images 
                --name Model_Nameorpython train.py --dataroot /path/to/input/images 
                --name Model_NameTo view training losses and results, open the URLhttp://localhost:8097. For cloud servers replace localhost with your IP.Epoch-wise intermediate training results are inDeepLIIF/checkpoints/Model_Name/web/index.html.Trained models will be by default be saved inDeepLIIF/checkpoints/Model_Name.Training datasets can be downloadedhere.DP: To train a model you can use DP. DP is single-process. It means thatall the GPUs you want to use must be on the same machineso 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 DPhere.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 1Note thatbatch-sizeis defined per process. Since DP is a single-process method, thebatch-sizeyou set is theeffectivebatch 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 (seedoc).
Despite all the benefits of DDP, one drawback is the extra GPU memory needed for dedicated CUDA buffer for communication. See a short discussionhere. In the context of DeepLIIF, this means that there might be situations where you could use abigger batch size with DPas compared to DDP, which may actually train faster than using DDP with a smaller batch size.
You can find more information on DDPhere.To launch training using DDP on a local machine, usedeepliif 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 thatbatch-sizeis defined per process. Since DDP is a single-process method, thebatch-sizeyou set is the batch size for each process, and theeffectivebatch size will bebatch-sizemultiplied by the number of processes you started. In the above example, it will be 3 * 2 = 6.You still need to provideall GPU ids to useto the training command. Internally, in each process DeepLIIF picks the device usinggpu_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.-t 3 --log_dir <log_dir>is not required, but is a useful setting intorchrunthat 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"If your PyTorch is older than 1.10, DeepLIIF callstorch.distributed.launchin the backend. Otherwise, DeepLIIF callstorchrun.Serialize ModelThe installeddeepliifuses Dask to perform inference on the input IHC images.
Before running thetestcommand, 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/filesBy default, the model files are expected to be located inDeepLIIF/model-server/DeepLIIF_Latest_Model.By default, the serialized files will be saved to the same directory as the input model files.TestingTo test the model:deepliif test --input-dir /path/to/input/images 
              --output-dir /path/to/output/images 
              --model-dir path/to/the/serialized/model
              --tile-size 512orpython test.py --dataroot /path/to/input/images 
               --name Model_NameThe latest version of the pretrained models can be downloadedhere.Before running test on images, the model files must be serialized as described above.The serialized model files are expected to be located inDeepLIIF/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 downloadedhere.Whole Slide Image (WSI) Inference:For translation and segmentation of whole slide images,
you can simply use the same test command
giving path to the directory containing your whole slide images as the input-dir.
DeepLIIF automatically reads the WSI region by region,
and translate and segment each region separately and stitches the regions
to create the translation and segmentation for whole slide image,
then saves all masks in the format of ome.tiff in the given output-dir.
Based on the available GPU resources, the region-size can be changed.deepliif test --input-dir /path/to/input/images 
              --output-dir /path/to/output/images 
              --model-dir path/to/the/serialized/model
              --tile-size 512
              --region-size 20000If you prefer, it is possible to run the models using Torchserve.
Please seethe documentationon how to deploy the model with Torchserve and for an example of how to run the inference.DockerWe provide a Dockerfile that can be used to run the DeepLIIF models inside a container.
First, you need to install theDocker Engine.
After installing the Docker, you need to follow these steps:Download the pretrained modelhereand place them in DeepLIIF/model-server/DeepLIIF_Latest_Model.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 --tile-size 512When you run a container from the image, thedeepliifCLI 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.ImageJ PluginIf you don't have access to GPU or appropriate hardware and just want to use ImageJ to run inference, we have also created anImageJ pluginfor your convenience.The plugin also supports submitting multiple ROIs at once:Cloud DeploymentIf you don't have access to GPU or appropriate hardware and don't want to install ImageJ, we have also created acloud-native DeepLIIF deploymentwith a user-friendly interface to upload images, visualize, interact, and download the final results.DeepLIIF can also be accessed programmatically through an endpoint by posting a multipart-encoded request
containing the original image file:POST /api/infer

Parameters

img (required)
file: image to run the models on

resolution
string: resolution used to scan the slide (10x, 20x, 40x), defaults to 40x 

pil
boolean: if true, use PIL.Image.open() to load the image, instead of python-bioformats

slim
boolean: if true, return only the segmentation result imageFor example, in Python:importosimportjsonimportbase64fromioimportBytesIOimportrequestsfromPILimportImage# Use the sample images from the main DeepLIIF repoimages_dir='./Sample_Large_Tissues'filename='ROI_1.png'res=requests.post(url='https://deepliif.org/api/infer',files={'img':open(f'{images_dir}/{filename}','rb')},# optional param that can be 10x, 20x, or 40x (default)params={'resolution':'40x'})data=res.json()defb64_to_pil(b):returnImage.open(BytesIO(base64.b64decode(b.encode())))forname,imgindata['images'].items():output_filepath=f'{images_dir}/{os.path.splitext(filename)[0]}_{name}.png'withopen(output_filepath,'wb')asf:b64_to_pil(img).save(f,format='PNG')print(json.dumps(data['scoring'],indent=2))Synthetic Data GenerationThe 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 below.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 modelhere.RegistrationTo 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.Contributing Training DataTo 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 1264 co-registered sets of IHC and corresponding multiplex images of
size 512x512. We randomly selected 575 sets for training, 91 sets for validation, and 598 sets for testing the model.
We also randomly selected and manually segmented 41 images of size 640x640 from recently releasedBCDatasetwhich 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 datasethere.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 viaImPartial, train the
DeepLIIF model and release back to the community with full credit to the contributors.Memorial Sloan Kettering Cancer CenterAI-ready immunohistochemistry and multiplex immunofluorescence datasetfor breast, lung, and bladder cancers (Nature Machine Intelligence'22)Moffitt Cancer CenterAI-ready multiplex immunofluorescence and multiplex immunohistochemistry datasetfor head-and-neck squamous cell carcinoma (MICCAI'23)SupportPlease use theImage.sc Forumfor discussion and questions related to DeepLIIF.Bugs can be reported in theGitHub Issuestab.LicenseÂ©Nadeem Lab- DeepLIIF code is distributed underApache 2.0 with Commons Clauselicense,
and is available for non-commercial academic purposes.AcknowledgmentsThis code is inspired byCycleGAN and pix2pix in PyTorch.ReferenceIf you find our work useful in your research or if you use parts of this code or our released dataset, please cite the following papers:@article{ghahremani2022deep,
  title={Deep learning-inferred multiplex immunofluorescence for immunohistochemical 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},
  volume={4},
  number={4},
  pages={401--412},
  year={2022},
  publisher={Nature Publishing Group}
}

@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={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={21399--21405},
  year={2022}
}

@article{ghahremani2023deepliifdataset,
  title={An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment},
  author={Ghahremani, Parmida and Marino, Joseph and Hernandez-Prera, Juan and V. de la Iglesia, Janis and JC Slebos, Robbert and H. Chung, Christine and Nadeem, Saad},
  journal={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year={2023}
}

@article{nadeem2023ki67validationMTC,
  author = {Nadeem, Saad and Hanna, Matthew G and Viswanathan, Kartik and Marino, Joseph and Ahadi, Mahsa and Alzumaili, Bayan and Bani, Mohamed-Amine and Chiarucci, Federico and Chou, Angela and De Leo, Antonio and Fuchs, Talia L and Lubin, Daniel J and Luxford, Catherine and Magliocca, Kelly and Martinez, GermÃ¡n and Shi, Qiuying and Sidhu, Stan and Al Ghuzlan, Abir and Gill, Anthony J and Tallini, Giovanni and Ghossein, Ronald and Xu, Bin},
  title = {Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platforms},
  journal = {Histopathology},
  year = {2023},
  doi = {https://doi.org/10.1111/his.15048}
}