add graphical abstract

README.md

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 # 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, <https://www.cell.com/cell-reports/pdf/S2211-1247(21)01170-0.pdf>) 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 <https://lightsheetatlas.pni.princeton.edu/public/brainpipe_demo_datasets/>. 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 <https://brainmaps.princeton.edu/2020/09/princeton-mouse-brain-atlas-links/>. Aligned viral tracing injection data from Pisano et al. 2021, <https://www.cell.com/cell-reports/pdf/S2211-1247(21)01170-0.pdf> 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)