Description

A pre-trained model for inferencing volumetric (3D) kidney substructures segmentation from contrast-enhanced CT images (Arterial/Portal Venous Phase).

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:

Method and Network

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

Details of the architecture:

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.

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 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

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