# **FLASH**: **F**ramework for **LA**rge-**S**cale **H**istomorphometry
# **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 histopathology data. In our experiments, we utilized human tissue data sampled from different cohorts including inhouse biopsies (UKA_B) and nephrectomies (UKA_N), HuBMAP, KPMP, and VALIGA.
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).
# Installation
# Installation
1. Clone this repo using [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git):<br>
1. Clone this repo using [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git):<br>
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@@ -46,17 +46,14 @@ You can also apply the trained network to our provided exemplary image patches c
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@@ -46,17 +46,14 @@ You can also apply the trained network to our provided exemplary image patches c