Update README.md

README.md

@@ -5,7 +5,7 @@
 
 # **FLASH**: **F**ramework for **LA**rge-**S**cale **H**istomorphometry
 
-This repository represents a python framework to train, evaluate and apply segmentation networks for renal histological analysis. In particular, we trained an [nnUnet](https://github.com/MIC-DKFZ/nnUNet) for kidney tissue segmentation followed by training another [U-net-like](https://arxiv.org/pdf/1505.04597.pdf) CNN for the segmentation of several renal structures including ![#ff0000](https://via.placeholder.com/15/ff0000/000000?text=+) tubulus, ![#00ff00](https://via.placeholder.com/15/00ff00/000000?text=+) glomerulus, ![#0000ff](https://via.placeholder.com/15/0000ff/000000?text=+) glomerular tuft, ![#00ffff](https://via.placeholder.com/15/00ffff/000000?text=+) non-tissue background (including veins, renal pelvis), ![#ff00ff](https://via.placeholder.com/15/ff00ff/000000?text=+) artery, and ![#ffff00](https://via.placeholder.com/15/ffff00/000000?text=+) arterial lumen from PAS-stained histopathology data. In our experiments, we utilized human tissue data sampled from different cohorts including inhouse biopsies (UKA_B) and nephrectomies (UKA_N), the *Human BioMolecular Atlas Program* cohort (HuBMAP), the *Kidney Precision Medicine Project* cohort (KPMP), and the *Validation of the Oxford classification of IgA Nephropathy* cohort (VALIGA).
+This repository represents a python framework to train, evaluate and apply segmentation networks for renal histological analysis. In particular, we trained an [nnUnet](https://github.com/MIC-DKFZ/nnUNet) for kidney tissue segmentation followed by training another [U-net-like](https://arxiv.org/pdf/1505.04597.pdf) CNN for the segmentation of several renal structures including ![#ff0000](https://via.placeholder.com/15/ff0000/000000?text=+) tubulus, ![#00ff00](https://via.placeholder.com/15/00ff00/000000?text=+) glomerulus, ![#0000ff](https://via.placeholder.com/15/0000ff/000000?text=+) glomerular tuft, ![#00ffff](https://via.placeholder.com/15/00ffff/000000?text=+) non-tissue background (including veins, renal pelvis), ![#ff00ff](https://via.placeholder.com/15/ff00ff/000000?text=+) artery, and ![#ffff00](https://via.placeholder.com/15/ffff00/000000?text=+) arterial lumen from PAS-stained histopathology data. In our experiments, we utilized human tissue data sampled from different cohorts including inhouse biopsies (AC_B) and nephrectomies (AC_N), the *Human BioMolecular Atlas Program* cohort (HuBMAP), the *Kidney Precision Medicine Project* cohort (KPMP), and the *Validation of the Oxford classification of IgA Nephropathy* cohort (VALIGA).
 
 # Installation
 1. Clone this repo using [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git):<br>
@@ -55,9 +55,9 @@ Besides, you can also apply the trained network to our provided exemplary image
 <br>
 | Cohort | Annotation |
 |:--:|:--:|
-| UKA_B | Annotation |
+| AC_B | Annotation |
 | <img src="/exemplaryImages/UKA_Biopsies.png" width="400">| <img src="/exemplaryImages/UKA_Biopsies_Annotation.png" width="324"> |
-| UKA_N | Annotation |
+| AC_N | Annotation |
 | <img src="/exemplaryImages/UKA_Nephrectomy.png" width="400">| <img src="/exemplaryImages/UKA_Nephrectomy_Annotation.png" width="324"> |
 | HuBMAP | Annotation |
 | <img src="/exemplaryImages/HuBMAP.png" width="400">| <img src="/exemplaryImages/HuBMAP_Annotation.png" width="324"> |