diff --git a/README.md b/README.md index 263c08ff580b74bde944e5077f014d85d6f0f160..cf1da0fea48fc4ffaf559ec83ae627174a49a46f 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # KidneyStainTranslation This repository provides a framework to train CycleGAN- and U-GAT-IT-based translators for unsupervised stain-to-stain translation in histology. It builds upon this [CycleGAN repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). <br> -An exemplary translation from the aSMA to PAS stain in kidney tissue is provided below and its applicability by a prior segmentation model is shown by comparison with the ground-truth. The employed segmentation model is from this [paper](https://jasn.asnjournals.org/content/32/1/52.abstract) and its code repo can be found [here](https://github.com/NBouteldja/KidneySegmentation_Histology). In summary, its based on the [U-net architecture](https://arxiv.org/pdf/1505.04597.pdf) and was trained to segment several renal structures including tubulus , glomerulus , glomerular tuft , vein (including renal pelvis) , artery , and arterial lumen  from kidney histopathology data. +An exemplary translation from the aSMA to PAS stain in kidney tissue is provided below and its applicability by a prior segmentation model is shown by comparison with the ground-truth. The employed segmentation model is from this [paper](https://jasn.asnjournals.org/content/32/1/52.abstract) and its code repo can be found [here](https://github.com/NBouteldja/KidneySegmentation_Histology). In summary, its based on the [U-net architecture](https://arxiv.org/pdf/1505.04597.pdf) and was trained to segment several renal structures including tubulus (colorful), glomerulus , glomerular tuft , vein (including renal pelvis) , artery , and arterial lumen  from kidney histopathology data. <br> | Input aSMA image | Fake PAS translation | |:--:|:--:|