diff --git a/README.md b/README.md index 7de40be0bba81416eba497d0bfce73fd782a6b5d..0e2d8921652ff3008b661cc7510ee025fea588d6 100644 --- a/README.md +++ b/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  tubulus,  glomerulus,  glomerular tuft,  non-tissue background (including veins, renal pelvis),  artery, and  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  tubulus,  glomerulus,  glomerular tuft,  non-tissue background (including veins, renal pelvis),  artery, and  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. ```