From 450ad0869036a34842732336545ca22a4d059ec7 Mon Sep 17 00:00:00 2001 From: Austin Hoag Date: Thu, 11 Nov 2021 11:47:58 -0500 Subject: [PATCH] add graphical abstract --- README.md | 9 ++++++++- static/SW_GrAbstr_STAR_1n.png | Bin 0 -> 129869 bytes 2 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 static/SW_GrAbstr_STAR_1n.png diff --git a/README.md b/README.md index 9cbe288..b69b1d3 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,15 @@ # BrainPipe: Registration and object detection pipelines for three-dimensional whole-brain analysis. - Includes three-dimensional convolutional neural network (CNN) with a U-Net architecture (Gornet et al., 2019; K. Lee, Zung, Li, Jain, & Sebastian Seung, 2017) with added packages developed by Kisuk Lee (Massachusetts Institute of Technology), Nick Turner (Princeton University), James Gornet (Columbia University), and Kannan Umadevi Venkatarju (Cold Spring Harbor Laboratories). +![plot](./static/SW_GrAbstr_STAR_1n.png) + +BrainPipe (Pisano et al. 2021, ) is an automated transsynaptic tracing pipeline that combines anatomical assignment using volumetric registration and injection site segmentation with convolutional neural networks for cell detection. After installing using this README's instructions, please see EXAMPLES.md for basic BrainPipe use cases. Demonstration datasets to ensure proper usage of BrainPipe and trained CNNs for H129 and PRV detection can be found here . Note, trained CNN's are a good starting point for transfer learning, but probably will not work out-of-the-box for other projects' datasets. For a deeper-dive into the BrainPipe pipeline, please see IMPORTANT_FILES.md for details. When starting to use your own data, BrainPipe expects certain formatting of images. Please see FILE-FORMATTING.md for details. + +The Princeton Mouse Atlas data can be found here . Aligned viral tracing injection data from Pisano et al. 2021, has been deposited at https://brainmaps.princeton.edu/2021/05/pisano_viral_tracing_injections/. + ### CNN Dependencies (see install instructions below): +Includes three-dimensional convolutional neural network (CNN) with a U-Net architecture (Gornet et al., 2019; K. Lee, Zung, Li, Jain, & Sebastian Seung, 2017) with added packages developed by Kisuk Lee (Massachusetts Institute of Technology), Nick Turner (Princeton University), James Gornet (Columbia University), and Kannan Umadevi Venkatarju (Cold Spring Harbor Laboratories). + [DataProvider3](https://github.com/torms3/DataProvider3) [PyTorchUtils](https://github.com/nicholasturner1/PyTorchUtils) [Augmentor](https://github.com/torms3/Augmentor) diff --git a/static/SW_GrAbstr_STAR_1n.png b/static/SW_GrAbstr_STAR_1n.png new file mode 100644 index 0000000000000000000000000000000000000000..0982774b806c44dc248c28811d140f1c89068553 GIT binary patch literal 129869 zcmd43WmH^C(*_Cz4+H{9aCd?PIJirI!JXjl?he62@IeLw!3nOxWpJ0l-3E7ey_56G zciw;Z*Ijqk+H3FCGrMi5cqmMT+G*pphqVG4tF*NKS z`N)9o?4k1QTa=1nz+gLO7$L@oh=CrY7L3SWYT z=8Rk|3^(r!FyzARX92sS>cDk}Exbkuieb0|6rq!^+Td$zYg+<$QG&9lYjY4K_;>Ks zKA8QoNTlaM5NA~H;9%8Klh_G2Ua@G^Ep4r^jeLd2dRGCIl&XD zMl~-*nrdlOyxfd{1%H|*B~+EJYdsvd*7I{TpZFBCo|xB3Op`~@h__>ZLd1ao*C|jT 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