Update README.md

The model in this repository is used to evaluate computer vision as a tool for analyzing floral micro-CT datasets of cacao (Theobroma cacao). It accompanies code on GitHub (https://github.com/aubricot/CV_for_flower_CT). Code is built in Google Colab Notebooks with Python 3 and MONAI Core 1.5. Micro-CT datasets and labels were generated in 3D Slicer as .nrrd files. They are converted to NIFTI (.nii.gz) format and registered to eachother in preprocessing_whole_flower_nrrd2nifti.ipynb. UNETR is trained to recognize whole flower outlines from CT data in Cacao_Whole_Flower_Seg_unetr_train.ipynb. Results are visualized on input and output images to generate figures and interpret results in Cacao_Whole_Flower_Seg_unetr_inspect_results.ipynb.

README.md

@@ -1,3 +1,13 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-segmentation
+tags:
+- monai
+- unetr
+- unet
+- flower
+- microCT
+- tomography
+- microscopy
+- 3D
+---