From 1c79c96dd8b82acd70594ce2335529b2987584b5 Mon Sep 17 00:00:00 2001 From: Pete Date: Wed, 11 Sep 2024 09:29:12 +0100 Subject: [PATCH] Update README.md --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b7eb292..b86f3b7 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,12 @@ InstanSeg logo +## 🚧 Work-in-progress - please wait! 🚧 + +**We're preparing a QuPath release candidate to make this extension available for testing - please come back in a day or two!** + +--- + **Welcome to the [InstanSeg](https://github.com/instanseg/instanseg) extension for [QuPath](http://qupath.github.io)!** **InstanSeg** is a novel deep-learning-based method for segmenting nuclei and cells... and potentially much more. @@ -31,7 +37,7 @@ To read about InstanSeg's extension to _nucleus + full cell segmentation_ and su - One model can provide different outputs: nuclei, cells, or both 3. It's accurate compared to all the popular alternative methods - In our hands InstanSeg consistently achieved the best F1 score across multiple datasets compared to CellPose, StarDist, HoVerNet and Mesmer. But everyone's images are different & fair benchmarking is hard - check out the preprints & judge what works best for you! -4. It's *much* faster than other methods +4. It's faster than other methods (usually _much_ faster) - InstanSeg supports GPU acceleration with CUDA _and_ with Apple Silicon (so Mac users can finally have fast segmentation too!) 5. It's portable - The full pipeline _including postprocessing_ compiles to TorchScript - so you can also run it from [Python](https://github.com/instanseg/instanseg) & [DeepImageJ](https://deepimagej.github.io).