Update README.md

README.md

@@ -4,7 +4,7 @@
 
 # **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 a neural network based on the [U-net architecture](https://arxiv.org/pdf/1505.04597.pdf) to segment 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 (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).
 
 # Installation
 1. Clone this repo using [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git):<br>
@@ -24,7 +24,7 @@ source activate python37
 conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
 ```
 # Training
-Train a network, e.g. using the following command:
+Train a structure segmentation network, e.g. using the following command:
 ```
 python ./FLASH/training.py -m custom -s train_val_test -e 500 -b 6 -r 0.001 -w 0.00001
 ```
@@ -34,6 +34,7 @@ Note:<br>
 ```
 training.py --model --setting --epochs --batchSize --lrate --weightDecay 
 ```
+- We trained the prior tissue segmentation network using the [nnUnet repo](https://github.com/MIC-DKFZ/nnUNet).
 # Application
 Use *getPredictionForBigPatch.py* to apply the trained network for histopathological renal structure segmentation to data of your choice.
 ```