cellseg_models_pytorch 0.1.18

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.

Features

• High level API to define cell/nuclei instance segmentation models.
• 4 cell/nuclei instance segmentation models and more to come.
• Open source datasets for training and benchmarking.
• Pre-trained backbones/encoders from the timm library.
• All the architectures can be augmented to panoptic segmentation.
• A lot of flexibility to modify the components of the model architectures.
• Sliding window inference for large images.
• Multi-GPU inference.
• Popular training losses and benchmarking metrics.
• Simple model training with pytorch-lightning.
• Benchmarking utilities both for model latency & segmentation performance.
• Regularization techniques to tackle batch effects/domain shifts.
• Ability to add transformers to the decoder layers.

Installation

Basic installation

pip install cellseg-models-pytorch

To install extra dependencies (training utilities and datamodules for open-source datasets) use

pip install cellseg-models-pytorch[all]

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

Datasets

Dataset	Paper
[5, 6] Pannuke	https://arxiv.org/abs/2003.10778 , https://link.springer.com/chapter/10.1007/978-3-030-23937-4_2
[7] Lizard	http://arxiv.org/abs/2108.11195

Notebook examples

• Training Stardist with Pannuke. Train the Stardist model with constant sized Pannuke patches.
• Training Cellpose with Lizard. Train the Cellpose model with Lizard dataset that is composed of varying sized images.

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

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

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] 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).
• [6] 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.
• [7] 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.