From c01c699f16926b6fb0c9a810c20ae700a0988bb2 Mon Sep 17 00:00:00 2001 From: Nassim Bouteldja <nbouteldja@ukaachen.de> Date: Wed, 27 Apr 2022 18:01:07 +0200 Subject: [PATCH] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index aa28aae..2f5a4d1 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ # **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 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). +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 (AC_B) and nephrectomies (AC_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> @@ -55,9 +55,9 @@ Besides, you can also apply the trained network to our provided exemplary image <br> | Cohort | Annotation | |:--:|:--:| -| UKA_B | Annotation | +| AC_B | Annotation | | <img src="/exemplaryImages/UKA_Biopsies.png" width="400">| <img src="/exemplaryImages/UKA_Biopsies_Annotation.png" width="324"> | -| UKA_N | Annotation | +| AC_N | Annotation | | <img src="/exemplaryImages/UKA_Nephrectomy.png" width="400">| <img src="/exemplaryImages/UKA_Nephrectomy_Annotation.png" width="324"> | | HuBMAP | Annotation | | <img src="/exemplaryImages/HuBMAP.png" width="400">| <img src="/exemplaryImages/HuBMAP_Annotation.png" width="324"> | -- GitLab