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SKINET Project is meant to perform a segmentation of a kidney's biopsy or a nephrectomy and recognize the different histological structures.

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SKINET (Segmentation of the KIdney through a Neural nETwork) Project

SKINET Project is meant to provide a segmentation of the different structures in kidney histological tissue (biopsy or nephrectomy). It allows the segmentation of sclerotic and non sclerotic glomeruli, healthy or atrophic tubules, veins... This repository contains all the project's code. You can use our online inference tool to test our tool on your biopsies (tutorial in the "docs" folder). You can also create a local version of this project by cloning this repo and installing a suitable environment (tutorial in the "docs" folder).

The project's code is based on Matterport's Mask R-CNN and Navidyou's repository.

This project is a collaboration between a Nephrology team from Dijon Burgundy Teaching Hospital, LEAD Laboratory, and a student from ESIREM, all located in Dijon, Burgundy, France.

Inference tool

Last : Open Inference Tool In Colab

v1.0 : Open Inference Tool In Colab

v1.1.1 : Open Inference Tool In Colab

Update: Unfortunately, google collab doesn't support tensorflow 1* anymore. The online version of our tool is no longer available. We are currently working on providing a new version. In the mean time, you can still clone this repo and use a local version.

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SKINET Project is meant to perform a segmentation of a kidney's biopsy or a nephrectomy and recognize the different histological structures.

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