cellseg_models_pytorch 0.1.25

Python library for 2D cell/nuclei instance segmentation models written with PyTorch.

Python library for 2D cell/nuclei instance segmentation models written with PyTorch.

Introduction

cellseg-models.pytorch is a library built upon PyTorch that contains multi-task encoder-decoder architectures along with dedicated post-processing methods for segmenting cell/nuclei instances. As the name might suggest, this library is heavily inspired by segmentation_models.pytorch library for semantic segmentation.

What's new? 📢

• Now you can use any pre-trained image encoder from the timm library as the model backbone. (Given that they implement the forward_intermediates method, most of them do).
• New example notebooks showing how to finetune Cellpose and Stardist with the new state-of-the-art foundation model backbones: UNI from the MahmoodLab, and Prov-GigaPath from Microsoft Research. Check out the notebooks here (UNI), and here (Prov-GigaPath).
• NOTE!: These foundation models are licensed under restrictive licences and you need to agree to the terms of any of the said models to get access to the weights. Once you have been granted access, you can run the above notebooks. You can request for access here: the model pages UNI and model pages Prov-GigaPath. These models may only be used for non-commercial, academic research purposes with proper attribution. Be sure that you have read and understood the terms before requesting access.

Features 🌟

• High level API to define cell/nuclei instance segmentation models.
• 6 cell/nuclei instance segmentation model architectures and more to come.
• Open source datasets for training and benchmarking.
• Flexibility to modify the components of the model architectures.
• Sliding window inference for large images.
• Multi-GPU inference.
• All model architectures can be augmented to panoptic segmentation.
• Popular training losses and benchmarking metrics.
• Benchmarking utilities both for model latency & segmentation performance.
• Regularization techniques to tackle batch effects/domain shifts such as Strong Augment, Spectral decoupling, Label smoothing.
• Example notebooks to train models with lightning or accelerate.
• Example notebooks to finetune models with foundation model backbones such as UNI, Prov-GigaPath, and DINOv2.

Installation 🛠️

pip install cellseg-models-pytorch

Models 🤖

Model	Paper
[1] HoVer-Net	https://www.sciencedirect.com/science/article/pii/S1361841519301045?via%3Dihub
[2] Cellpose	https://www.nature.com/articles/s41592-020-01018-x
[3] Omnipose	https://www.biorxiv.org/content/10.1101/2021.11.03.467199v2
[4] Stardist	https://arxiv.org/abs/1806.03535
[5] CellVit-SAM	https://arxiv.org/abs/2306.15350.03535
[6] CPP-Net	https://arxiv.org/abs/2102.0686703535

Datasets

Dataset	Paper
[7, 8] Pannuke	https://arxiv.org/abs/2003.10778 , https://link.springer.com/chapter/10.1007/978-3-030-23937-4_2

Notebook examples 👇

Finetuning CellPose with UNI backbone

• Finetuning CellPose with UNI. Here we finetune the CellPose multi-class nuclei segmentation model with the foundation model UNI-image-encoder backbone (checkout UNI). The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing accelerate by hugginface. NOTE that you need to have granted access to the UNI weights and agreed to the terms of the model to be able to run the notebook.

Finetuning Stardist with Prov-GigaPath backbone

• Finetuning Stardist with Prov-GigaPath. Here we finetune the Stardist multi-class nuclei segmentation model with the foundation model Prov-GigaPath-image-encoder backbone (checkout Prov-GigaPath). The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing accelerate by hugginface. NOTE that you need to have granted access to the Prov-GigaPath weights and agreed to the terms of the model to be able to run the notebook.

Finetuning CellPose with DINOv2 backbone

• Finetuning CellPose with DINOv2 backbone for Pannuke. Here we finetune the CellPose multi-class nuclei segmentation model with a LVD-142M pretrained DINOv2 backbone. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing lightning.

Finetuning CellVit-SAM with Pannuke

• Finetuning CellVit-SAM with Pannuke. Here we finetune the CellVit-SAM multi-class nuclei segmentation model with a SA-1B pretrained SAM-image-encoder backbone (checkout SAM). The encoder is transformer based VitDet-model. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing accelerate by hugginface.

Training Hover-Net with Pannuke

• Training Hover-Net with Pannuke. Here we train the Hover-Net nuclei segmentation model with an imagenet pretrained resnet50 backbone from the timm library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained by utilizing lightning.

Training Stardist with Pannuke

• Training Stardist with Pannuke. Here we train the Stardist multi-class nuclei segmentation model with an imagenet pretrained efficientnetv2_s backbone from the timm library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained by utilizing lightning.

Training CellPose with Pannuke

• Training CellPose with Pannuke. Here we train the CellPose multi-class nuclei segmentation model with an imagenet pretrained convnext_small backbone from the timm library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing accelerate by hugginface.

Training OmniPose with Pannuke

• Training OmniPose with Pannuke. Here we train the OmniPose multi-class nuclei segmentation model with an imagenet pretrained focalnet_small_lrf backbone from the timm library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained (with checkpointing) by utilizing accelerate by hugginface.

Training CPP-Net with Pannuke

• Training CPP-Net with Pannuke. Here we train the CPP-Net multi-class nuclei segmentation model with an imagenet pretrained efficientnetv2_s backbone from the timm library. The Pannuke dataset (fold 1 & fold 2) are used for training data and the fold 3 is used as validation data. The model is trained by utilizing lightning.

Benchmarking Cellpose Trained on Pannuke

• Benchmarking Cellpose Trained on Pannuke. Here we run benchmarking for Cellpose that was trained on Pannuke. Both the model performance and latency benchmarking are covered.

Code Examples 💻

Define Cellpose for cell segmentation.

import cellseg_models_pytorch as csmp
import torch

model = csmp.models.cellpose_base(type_classes=5)
x = torch.rand([1, 3, 256, 256])

# NOTE: the outputs still need post-processing.
y = model(x) # {"cellpose": [1, 2, 256, 256], "type": [1, 5, 256, 256]}

Define Cellpose for cell and tissue area segmentation (Panoptic segmentation).

import cellseg_models_pytorch as csmp
import torch

model = csmp.models.cellpose_plus(type_classes=5, sem_classes=3)
x = torch.rand([1, 3, 256, 256])

# NOTE: the outputs still need post-processing.
y = model(x) # {"cellpose": [1, 2, 256, 256], "type": [1, 5, 256, 256], "sem": [1, 3, 256, 256]}

Define panoptic Cellpose model with more flexibility.

import cellseg_models_pytorch as csmp

# the model will include two decoder branches.
decoders = ("cellpose", "sem")

# and in total three segmentation heads emerging from the decoders.
heads = {
    "cellpose": {"cellpose": 2, "type": 5},
    "sem": {"sem": 3}
}

model = csmp.CellPoseUnet(
    decoders=decoders,                   # cellpose and semantic decoders
    heads=heads,                         # three output heads
    depth=5,                             # encoder depth
    out_channels=(256, 128, 64, 32, 16), # num out channels at each decoder stage
    layer_depths=(4, 4, 4, 4, 4),        # num of conv blocks at each decoder layer
    style_channels=256,                  # num of style vector channels
    enc_name="resnet50",                 # timm encoder
    enc_pretrain=True,                   # imagenet pretrained encoder
    long_skip="unetpp",                  # unet++ long skips ("unet", "unetpp", "unet3p")
    merge_policy="sum",                  # concatenate long skips ("cat", "sum")
    short_skip="residual",               # residual short skips ("basic", "residual", "dense")
    normalization="bcn",                 # batch-channel-normalization.
    activation="gelu",                   # gelu activation.
    convolution="wsconv",                # weight standardized conv.
    attention="se",                      # squeeze-and-excitation attention.
    pre_activate=False,                  # normalize and activation after convolution.
)

x = torch.rand([1, 3, 256, 256])

# NOTE: the outputs still need post-processing.
y = model(x) # {"cellpose": [1, 2, 256, 256], "type": [1, 5, 256, 256], "sem": [1, 3, 256, 256]}

Run HoVer-Net inference and post-processing with a sliding window approach.

import cellseg_models_pytorch as csmp

# define the model
model = csmp.models.hovernet_base(type_classes=5)

# define the final activations for each model output
out_activations = {"hovernet": "tanh", "type": "softmax", "inst": "softmax"}

# define whether to weight down the predictions at the image boundaries
# typically, models perform the poorest at the image boundaries and with
# overlapping patches this causes issues which can be overcome by down-
# weighting the prediction boundaries
out_boundary_weights = {"hovernet": True, "type": False, "inst": False}

# define the inferer
inferer = csmp.inference.SlidingWindowInferer(
    model=model,
    input_folder="/path/to/images/",
    checkpoint_path="/path/to/model/weights/",
    out_activations=out_activations,
    out_boundary_weights=out_boundary_weights,
    instance_postproc="hovernet",               # THE POST-PROCESSING METHOD
    normalization="percentile",                 # same normalization as in training
    patch_size=(256, 256),
    stride=128,
    padding=80,
    batch_size=8,
)

inferer.infer()

inferer.out_masks
# {"image1" :{"inst": [H, W], "type": [H, W]}, ..., "imageN" :{"inst": [H, W], "type": [H, W]}}

Models API

Generally, the model building API enables the effortless creation of hard-parameter sharing multi-task encoder-decoder CNN architectures. The general architectural schema is illustrated in the below image.

Class API

The class API enables the most flexibility in defining different model architectures. It borrows a lot from segmentation_models.pytorch models API.

Model classes:

• csmp.CellPoseUnet
• csmp.StarDistUnet
• csmp.HoverNet
• csmp.CellVitSAM

All of the models contain:

• model.encoder - pretrained timm backbone for feature extraction.
• model.{decoder_name}_decoder - Models can have multiple decoders with unique names.
• model.{head_name}_seg_head - Model decoders can have multiple segmentation heads with unique names.
• model.forward(x) - forward pass.
• model.forward_features(x) - forward pass of the encoder and decoders. Returns enc and dec features

Defining your own multi-task architecture

For example, to define a multi-task architecture that has resnet50 encoder, four decoders, and 5 output heads with CellPoseUnet architectural components, we could do this:

import cellseg_models_pytorch as csmp
import torch

model = csmp.CellPoseUnet(
    decoders=("cellpose", "dist", "contour", "sem"),
    heads={
        "cellpose": {"type": 5, "cellpose": 2},
        "dist": {"dist": 1},
        "contour": {"contour": 1},
        "sem": {"sem": 4}
    },
)

x = torch.rand([1, 3, 256, 256])
model(x)
# {
#   "cellpose": [1, 2, 256, 256],
#   "type": [1, 5, 256, 256],
#   "dist": [1, 1, 256, 256],
#   "contour": [1, 1, 256, 256],
#   "sem": [1, 4, 256, 256]
# }

Function API

With the function API, you can build models with low effort by calling the below listed functions. Under the hood, the function API simply calls the above classes with pre-defined decoder and head names. The training and post-processing tools of this library are built around these names, thus, it is recommended to use the function API, although, it is a bit more rigid than the class API. Basically, the function API only lacks the ability to define the output-tasks of the model, but allows for all the rest as the class API.

Model functions	Output names	Task
csmp.models.cellpose_base	"type", "cellpose",	instance segmentation
csmp.models.cellpose_plus	"type", "cellpose", "sem",	panoptic segmentation
csmp.models.omnipose_base	"type", "omnipose"	instance segmentation
csmp.models.omnipose_plus	"type", "omnipose", "sem",	panoptic segmentation
csmp.models.hovernet_base	"type", "inst", "hovernet"	instance segmentation
csmp.models.hovernet_plus	"type", "inst", "hovernet", "sem"	panoptic segmentation
csmp.models.hovernet_small	"type","hovernet"	instance segmentation
csmp.models.hovernet_small_plus	"type", "hovernet", "sem"	panoptic segmentation
csmp.models.stardist_base	"stardist", "dist"	binary instance segmentation
csmp.models.stardist_base_multiclass	"stardist", "dist", "type"	instance segmentation
csmp.models.stardist_plus	"stardist", "dist", "type", "sem"	panoptic segmentation
csmp.models.cppnet_base	"stardist_refined", "dist"	binary instance segmentation
csmp.models.cppnet_base_multiclass	"stardist_refined", "dist", "type"	instance segmentation
csmp.models.cppnet_plus	"stardist_refined", "dist", "type", "sem"	panoptic segmentation
csmp.models.cellvit_sam_base	"type", "inst", "hovernet"	instance segmentation
csmp.models.cellvit_sam_plus	"type", "inst", "hovernet", "sem"	panoptic segmentation
csmp.models.cellvit_sam_small	"type","hovernet"	instance segmentation
csmp.models.cellvit_sam_small_plus	"type", "hovernet", "sem"	panoptic segmentation

References

• [1] S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." Medical Image Analysis, Sept. 2019.
• [2] Stringer, C.; Wang, T.; Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation Nature Methods, 2021, 18, 100-106
• [3] Cutler, K. J., Stringer, C., Wiggins, P. A., & Mougous, J. D. (2022). Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. bioRxiv. doi:10.1101/2021.11.03.467199
• [4] Uwe Schmidt, Martin Weigert, Coleman Broaddus, & Gene Myers (2018). Cell Detection with Star-Convex Polygons. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II (pp. 265–273).
• [5] Hörst, F., Rempe, M., Heine, L., Seibold, C., Keyl, J., Baldini, G., Ugurel, S., Siveke, J., Grünwald, B., Egger, J., & Kleesiek, J. (2023). CellViT: Vision Transformers for Precise Cell Segmentation and Classification (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2306.15350.
• [6] Chen, S., Ding, C., Liu, M., Cheng, J., & Tao, D. (2023). CPP-Net: Context-Aware Polygon Proposal Network for Nucleus Segmentation. In IEEE Transactions on Image Processing (Vol. 32, pp. 980–994). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tip.2023.3237013
• [7] Gamper, J., Koohbanani, N., Benet, K., Khuram, A., & Rajpoot, N. (2019) PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification. In European Congress on Digital Pathology (pp. 11-19).
• [8] Gamper, J., Koohbanani, N., Graham, S., Jahanifar, M., Khurram, S., Azam, A.,Hewitt, K., & Rajpoot, N. (2020). PanNuke Dataset Extension, Insights and Baselines. arXiv preprint arXiv:2003.10778.
• [9] Graham, S., Jahanifar, M., Azam, A., Nimir, M., Tsang, Y.W., Dodd, K., Hero, E., Sahota, H., Tank, A., Benes, K., & others (2021). Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 684-693).

Citation

@misc{csmp2022,
    title={{cellseg_models.pytorch}: Cell/Nuclei Segmentation Models and Benchmark.},
    author={Oskari Lehtonen},
    howpublished = {\url{https://github.com/okunator/cellseg_models.pytorch}},
    doi = {10.5281/zenodo.7064617}
    year={2022}
}

Licence

This project is distributed under MIT License

The project contains code from the original cell segmentation and 3rd-party libraries that have permissive licenses:

• timm (Apache-2)
• HoVer-Net (MIT)
• Cellpose (BSD-3)
• Stardist (BSD-3)

If you find this library useful in your project, it is your responsibility to ensure you comply with the conditions of any dependent licenses. Please create an issue if you think something is missing regarding the licenses.