From 784b10790e95efbc1f1ddfe8c29c45f4d290e31d Mon Sep 17 00:00:00 2001
From: Nassim Bouteldja <nbouteldja@ukaachen.de>
Date: Thu, 14 Apr 2022 00:04:14 +0200
Subject: [PATCH] Update README.md

---
 README.md | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 51ee6d0..1a5e594 100644
--- a/README.md
+++ b/README.md
@@ -40,10 +40,17 @@ Use *segment_WSI.py* to apply the trained networks for tissue and histopathologi
 ```
 python ./FLASH/segment_WSI.py
 ```
-Note: Before running the script, you need to specify the path to the image folder (variable: *WSIpath*), both network paths (variable: *modelpath*), and the results folder (variable: *resultspath*).<br>
-In particular, the script will segment a specified patch from the given WSI using the network. Determine the position of the patch of interest by providing the raw coodinates (e.g. coordinates shown in QuPath) of its upper left corner (variable: *patchCenterCoordinatesRaw*) and determine its size by modifying *patchGridCellTimes*. The latter variable specifies how many 516x516 patches are segmented row-wise as well as column-wise.<br>
+Note: Before running the script, you need to specify the path to the image folder (variable: *WSIrootFolder*), both network paths (variable: *model_FG_path* and *modelpath*), and the results folder (variable: *resultsPath*).<br>
+In particular, the script will recursively walk through all WSIs in a specified folder hierarchy and first apply the tissue segmentation CNN for segmentation. For this, the tissue is resampled to the expected pixel spacing by the CNN. The structure segmentation network is then applied to the detected tissue regions. After post-processing, the final prediction results are then saved using several (overlay) images as well as stored in numpy arrays for further feature analysis.
+# Feature computation
+Use *compute_features.py* to extract different morphological features from the predicted segmentations.
+```
+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.
+<br>
 <br>
-You can also apply the trained network to our provided exemplary image patches contained in the folder *exemplaryData*. These patches show various pathologies associated with different murine disease models, and are listed below including our ground-truth annotation:
+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:
 <br>
 | Cohort | Annotation |
 |:--:|:--:|
-- 
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