Cellprofiler app + plugin (#1724)

* first version of cellprofiler plugin

Signed-off-by: binliu <binliu@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* add vista support

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* fix some issues

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* update the plugin part

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix format

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* fix plugin doc

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* remove redundant imports

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* add the test case

Signed-off-by: Bin Liu (SW-GPU) <binliu@binliu-mlt.client.nvidia.com>

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* add mock test

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* add unit test

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* add readme for cellprofiler plugin

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* add the cellprofiler image

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* update readme file

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* remove the unnecessary file

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* fix import

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* add return

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* update vista app readme

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* rename the vista app to vista2d app

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* fix vista to vista2d

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* fix the bundle name from cell_vista_segmentation to vista2d

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* add vista2d model

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* fix readme issue

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---------

Signed-off-by: binliu <binliu@nvidia.com>
Signed-off-by: Bin Liu (SW-GPU) <binliu@binliu-mlt.client.nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Bin Liu (SW-GPU) <binliu@binliu-mlt.client.nvidia.com>

monailabel/tasks/infer/basic_infer.py

@@ -467,11 +467,10 @@ def _get_network(self, device, data):
                 if path:
                     checkpoint = torch.load(path, map_location=torch.device(device))
                     model_state_dict = checkpoint.get(self.model_state_dict, checkpoint)
-
-                    if set(self.network.state_dict().keys()) != set(checkpoint.keys()):
+                    if set(self.network.state_dict().keys()) != set(model_state_dict.keys()):
                         logger.warning(
                             f"Checkpoint keys don't match network.state_dict()! Items that exist in only one dict"
-                            f" but not in the other: {set(self.network.state_dict().keys()) ^ set(checkpoint.keys())}"
+                            f" but not in the other: {set(self.network.state_dict().keys()) ^ set(model_state_dict.keys())}"
                         )
                         logger.warning(
                             "The run will now continue unless load_strict is set to True. "

monailabel/utils/others/modelzoo_list.py

@@ -32,4 +32,5 @@
     "wholeBrainSeg_Large_UNEST_segmentation",  # whole brain segmentation for T1 MRI brain images. Added Oct 2022
     "lung_nodule_ct_detection",  # The first lung nodule detection task can be used for MONAI Label. Added Dec 2022
     "wholeBody_ct_segmentation",  # The SegResNet trained TotalSegmentator dataset with 104 tissues. Added Feb 2023
+    "vista2d",  # The VISTA segmentation trained on a collection of 15K public microscopy images. Added Aug 2024
 ]

plugins/cellprofiler/README.md

@@ -0,0 +1,51 @@
+<!--
+Copyright (c) MONAI Consortium
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MONAI Label Plugin for CellProfiler
+CellProfiler is a free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically.
+
+CellProfiler is designed to serve biologists as well as bioimage analysts who want a flexible system that is easy to deploy to collaborators who lack computational skills. It is commonly used for small-scale experiments involving a few images but is also routinely run on millions of images using cluster or cloud computing resources at some of the largest pharmaceutical companies and academic screening centers in the world.
+
+<img src="../../docs/images/cellprofiler.jpg" width=70% />
+
+### Table of Contents
+- [Supported Applications](#supported-applications)
+- [Installing CellProfiler](#installing-cellprofiler)
+- [Install MONAI Label Extension](#install-monai-label-extension)
+- [Using the Plugin](#using-the-plugin)
+
+### Supported Applications
+Users can find supported applications in the [sample-apps](../../sample-apps/vista2d/) folder and use the vista2d bundle. Currently, only this bundle can be used to create and refine labels for medical imaging tasks in CellProfiler.
+
+### Installing CellProfiler
+
+To use MONAILabel with CellProfiler, you first need to install CellProfiler from source code following the installation part of [CellProfiler WiKi](https://github.com/CellProfiler/CellProfiler/wiki). Once you have CellProfiler installed, you can install the MONAILabel plugin as shown in the `Install MONAI Label Extension` part.
+
+Please note these tips when installing the software:
+1. After cloned the source code, you must switch to a specific version tag of the repo to start installation.
+1. For Macbooks with Apple Silicon, please note [this issue](https://github.com/CellProfiler/CellProfiler/issues/4932) before you start to install.
+1. Before actually running the command `pip install -e .`, you may need to install numpy if it doesn't exist in your environment.
+
+### Install MONAI Label Extension
+1. Save the runvista2d.py file to a local path.
+1. Start CellProfiler from CLI, open the `File-->preferences` option and fill in the `CellProfiler plugin directory` parameter with your local path.
+
+## Using the Plugin
+
+1. Start the MONAI Label server with vista2d bundle.
+1. Add the `RunVISTA2D` module to your pipeline for object processing.
+1. Make sure the MONAILabel Server URL is correctly set in the plugin input.
+1. Click the `Analyse Images` or `Start Test Mode` button to execute the pipeline.
+1. Update masks and perform the sequential modules to get measurement results.
+
+<img src="../../docs/images/cellprofiler_plugin.png" width=70% />