From 39643a153994cd44e96e8f28b9aa494886775057 Mon Sep 17 00:00:00 2001 From: Ko Sugawara Date: Wed, 14 Jun 2023 20:02:42 +0900 Subject: [PATCH] Update README - add citation --- README.md | 30 ++++++++++++++++++++++++++++-- 1 file changed, 28 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9916cbf..b05177c 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,14 @@ # Segment Anything Models (SAM) API +![](https://github.com/ksugar/samapi/releases/download/assets/qupath-samapi.gif) + A web API for [SAM](https://github.com/facebookresearch/segment-anything) implemented with [FastAPI](https://fastapi.tiangolo.com). +This is a part of the following paper. Please [cite](#citation) it when you use this project. You will also cite [the original SAM paper](https://arxiv.org/abs/2304.02643). + +- 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 Create a conda environment. @@ -17,7 +24,7 @@ If you're using a computer with CUDA-compatible GPU, install `cudatoolkit`. conda install -y cudatoolkit=11.8 ``` -If you're using a computer with CUDA-compatible GPU on Windows, install `torch` with GPU-support here. +If you're using a computer with CUDA-compatible GPU on Windows, install `torch` with GPU-support with the following command. ```bash # Windows with CUDA-compatible GPU only @@ -78,4 +85,23 @@ Supporting other formats is a future work. ## Updates -- v0.2.0: Support for MPS backend (MacOS) by @petebankhead \ No newline at end of file +- v0.2.0: Support for MPS backend (MacOS) by [@petebankhead](https://github.com/petebankhead) + +## 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} +} +``` \ No newline at end of file