Update version to v0.2.0

README.md

@@ -1,9 +1,86 @@
 # QuPath extension Cellsparse
 
-## Install
+![](https://github.com/ksugar/qupath-extension-cellsparse/releases/download/assets/cellsparse-demo.gif)
 
-Drag and drop [the extension file](https://github.com/ksugar/qupath-extension-cellsparse/releases/download/v0.1.0/qupath-extension-cellsparse-0.1.0.jar) to [QuPath](https://qupath.github.io) and restart it.
+This is a QuPath extension for [Cellsparse API](https://github.com/ksugar/cellsparse-api).
+
+This is a part of the following paper. Please [cite](#citation) it when you use this project.
+
+- Sugawara, K. [*Training deep learning models for cell image segmentation with sparse annotations.*](https://biorxiv.org/cgi/content/short/2023.06.13.544786v1) bioRxiv 2023. doi:10.1101/2023.06.13.544786
+
+## Install & setup
+
+Drag and drop [the extension file](https://github.com/ksugar/qupath-extension-cellsparse/releases/download/v0.2.0/qupath-extension-cellsparse-0.2.0.jar) to [QuPath](https://qupath.github.io) and restart it.
 
 Set up the server following the instructions in the link below.
 
-[https://github.com/ksugar/cellsparse-api](https://github.com/ksugar/cellsparse-api)
+[https://github.com/ksugar/cellsparse-api](https://github.com/ksugar/cellsparse-api)
+
+If you use [SAM API](https://github.com/ksugar/samapi) together with this API, you need to use different ports for them.
+
+For example, the following command will launch [Cellsparse API](https://github.com/ksugar/cellsparse-api) on the port `8000` (default).
+
+```bash
+(cellsparse-api)$ uvicorn cellsparse_api.main:app
+INFO:     Started server process [26240]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+```
+
+Then, you can launch [SAM API](https://github.com/ksugar/samapi) on the port `18000` as follows.
+
+```bash
+(samapi) samapi.main:app --port 18000
+INFO:     Started server process [12060]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://127.0.0.1:18000 (Press CTRL+C to quit)
+```
+
+On QuPath, set the server URL for SAM (`Extensions` > `SAM` > `Server URL`) to `http://localhost:18000/sam/`.
+
+![](https://github.com/ksugar/qupath-extension-sam/releases/download/assets/qupath-sam-extension-server-url.png)
+
+## Usage
+
+Currently, [StarDist](https://stardist.net/index.html), [Cellpose](https://cellpose.readthedocs.io/en/latest/) and [ELEPHANT](https://elephant-track.github.io/) are available.
+
+### `Extensions` > `Cellsparse` > `[Algorithm]` > `Training`
+
+Train a model with the annotations in the current image.
+
+In the training, foreground and background annotations need to be assigned to the annotation classes with the specific names, `Foreground` and `Background`, respectively. These names are case-sensitive.
+
+![](https://github.com/ksugar/qupath-extension-cellsparse/releases/download/assets/qupath-extension-cellsparse-class-names.png)
+
+### `Extensions` > `Cellsparse` > `[Algorithm]` > `Inference`
+
+Run inference with the latest model.
+
+### `Extensions` > `Cellsparse` > `[Algorithm]` > `Reset`
+
+Reset a model (randomly initialized).
+
+### `Extensions` > `Cellsparse` > `[Algorithm]` > `Server URL`
+
+Set the server URL for [Cellsparse API](https://github.com/ksugar/cellsparse-api).
+
+## Citation
+
+Please cite my paper on [bioRxiv](https://biorxiv.org/cgi/content/short/2023.06.13.544786v1).
+
+```.bib
+@article {Sugawara2023.06.13.544786,
+	author = {Ko Sugawara},
+	title = {Training deep learning models for cell image segmentation with sparse annotations},
+	elocation-id = {2023.06.13.544786},
+	year = {2023},
+	doi = {10.1101/2023.06.13.544786},
+	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Deep learning is becoming more prominent in cell image analysis. However, collecting the annotated data required to train efficient deep-learning models remains a major obstacle. I demonstrate that functional performance can be achieved even with sparsely annotated data. Furthermore, I show that the selection of sparse cell annotations significantly impacts performance. I modified Cellpose and StarDist to enable training with sparsely annotated data and evaluated them in conjunction with ELEPHANT, a cell tracking algorithm that internally uses U-Net based cell segmentation. These results illustrate that sparse annotation is a generally effective strategy in deep learning-based cell image segmentation. Finally, I demonstrate that with the help of the Segment Anything Model (SAM), it is feasible to build an effective deep learning model of cell image segmentation from scratch just in a few minutes.Competing Interest StatementKS is employed part-time by LPIXEL Inc.},
+	URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786},
+	eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786.full.pdf},
+	journal = {bioRxiv}
+}
+```

pom.xml

@@ -4,7 +4,7 @@
 	<modelVersion>4.0.0</modelVersion>
 	<groupId>org.elephant</groupId>
 	<artifactId>qupath-extension-cellsparse</artifactId>
-	<version>0.1.0</version>
+	<version>0.2.0</version>
 	<dependencies>
 		<dependency>
 			<groupId>io.github.qupath</groupId>