---
tags:
- monai
- medical
library_name: monai
license: unknown
---
# Description
A pre-trained model for training and inferencing volumetric (3D) kidney substructures segmentation from contrast-enhanced CT images (Arterial/Portal Venous Phase). Training pipeline is provided to support model fine-tuning with bundle and MONAI Label active learning.

A tutorial and release of model for kidney cortex, medulla and collecting system segmentation.

Authors: Yinchi Zhou (yinchi.zhou@vanderbilt.edu) | Xin Yu (xin.yu@vanderbilt.edu) | Yucheng Tang (yuchengt@nvidia.com) |


# Model Overview
A pre-trained UNEST base model [1] for volumetric (3D) renal structures segmentation using dynamic contrast enhanced arterial or venous phase CT images.

## Data
The training data is from the [ImageVU RenalSeg dataset] from Vanderbilt University and Vanderbilt University Medical Center.
(The training data is not public available yet).

- Target: Renal Cortex | Medulla | Pelvis Collecting System
- Task: Segmentation
- Modality: CT (Artrial | Venous phase)
- Size: 96 3D volumes


The data and segmentation demonstration is as follow:

![](./renal.png) <br>

## Method and Network

The UNEST model is a 3D hierarchical transformer-based semgnetation network.

Details of the architecture:
![](./unest.png) <br>

## Training configuration
The training was performed with at least one 16GB-memory GPU.

Actual Model Input: 96 x 96 x 96

## Input and output formats
Input: 1 channel CT image

Output: 4: 0:Background, 1:Renal Cortex, 2:Medulla, 3:Pelvicalyceal System

## Performance
A graph showing the validation mean Dice for 5000 epochs.

![](./val_dice.png) <br>

This model achieves the following Dice score on the validation data (our own split from the training dataset):

Mean Valdiation Dice = 0.8523

Note that mean dice is computed in the original spacing of the input data.

## commands example
Download trained checkpoint model to ./model/model.pt:


Add scripts component:  To run the workflow with customized components, PYTHONPATH should be revised to include the path to the customized component:

```
export PYTHONPATH=$PYTHONPATH:"'<path to the bundle root dir>/scripts'"

```
Execute Training:

```
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
```

Execute inference:

```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
```


## More examples output

![](./demos.png) <br>


# Disclaimer
This is an example, not to be used for diagnostic purposes.

# References
[1] Yu, Xin, Yinchi Zhou, Yucheng Tang et al. "Characterizing Renal Structures with 3D Block Aggregate Transformers." arXiv preprint arXiv:2203.02430 (2022). https://arxiv.org/pdf/2203.02430.pdf

[2] Zizhao Zhang et al. "Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding." AAAI Conference on Artificial Intelligence (AAAI) 2022