From fb619626ed8cfbaa646c2c48f190864aadb8cd1f Mon Sep 17 00:00:00 2001
From: Nassim Bouteldja <nbouteldja@ukaachen.de>
Date: Mon, 11 Jul 2022 01:50:42 +0200
Subject: [PATCH] Update README.md

---
 README.md | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b1f98d9..c8ec205 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,8 @@ Use *compute_features.py* to extract different morphological features from the p
 ```
 python ./FLASH/compute_features.py
 ```
-Note: The script outputs a feature table for all structures in a *.csv* file. Each row contains features from a different instance and can thus be used to identify the instances.
+Note: The script outputs a feature table for all structures in a *.csv* file. Each row contains features from a different instance and can thus be used to identify the instances.<br>
+We applied this whole pipeline to multiple cohorts including AC_B, AC_N, HuBMAP, KPMP, and VALIGA, extracting about 40M features in total. We share those in the file *NGM_DataRepository.zip*. 
 <br>
 <br>
 Besides, you can also apply the trained network to our provided exemplary image patches contained in the folder *exemplaryData*. These patches show various pathologies and are listed below including our ground-truth annotation:
@@ -63,6 +64,10 @@ Besides, you can also apply the trained network to our provided exemplary image
 | <img src="/exemplaryImages/HuBMAP.png" width="400">| <br><br><img src="/exemplaryImages/HuBMAP_Annotation.png" width="324"> |
 | KPMP | Annotation |
 | <img src="/exemplaryImages/KPMP.png" width="400">| <br><br><img src="/exemplaryImages/KPMP_Annotation.png" width="324"> |
+<br>
+<b>Further notes:</b><br>
+- We showcase CNN segmentations of several thousand structures within the *exemplary_CNN_segmentations* folder.
+<br>
 
 # Contact
 Peter Boor, MD, PhD<br>
-- 
GitLab