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clara_pt_covid19_ct_lung_segmentation

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Description

A pre-trained model for volumetric (3D) segmentation of the lung from CT images.

Publisher

NVIDIA

Use Case

Segmentation

Framework

Clara Train

Latest Version

4.1

Modified

March 25, 2022

Size

210.56 MB

Model Overview

A pre-trained model for volumetric (3D) segmentation of the lung from CT images.

Note: The 4.1 version of this model is only compatible with the 4.1 version of the Clara Train SDK container

Model Architecture

The model is trained using a 3D anisotropic hybrid network [1].

Diagram showing the flow from model input, through the model architecture, and to model output

Training

The training was performed with the following:

  • Script: train.sh
  • GPU: at least 16GB of GPU memory.
  • Actual Model Input: 224 x 224 x 32
  • AMP: True
  • Optimizer: Adam
  • Learning Rate: 1e-4
  • Loss: DiceLoss

Dataset

This model was trained on a global dataset with a large experimental cohort collected from across the globe. The CT volumes of 120 independent subjects are provided by NIH with experts’ lung region annotations.

  • Target: Lung
  • Task: Segmentation
  • Modality: CT
  • Size: 120 3D volumes (90 Training, 10 Validation, 20 Testing)
  • Challenge: Large ranging foreground size

Performance

Dice score is used for evaluating the performance of the model. On the test set, the trained model achieved score of ~0.96 for lung.

Training

Training acc over 1250 epochs.

Graph that shows training acc over 1250 epochs

Validation

Validation mean dice over 1250 epochs.

Graph that shows validation mean dice getting higher over 1250 epochs until converging around 0.97

How to Use this Model

The model was validated with NVIDIA hardware and software. For hardware, the model can run on any NVIDIA GPU with memory greater than 16 GB. For software, this model is usable only as part of Transfer Learning & Annotation Tools in Clara Train SDK container. Find out more about Clara Train at the Clara Train Collections on NGC.

Full instructions for the training and validation workflow can be found in our documentation.

Input

Input: 1 channel CT image with intensity in HU and arbitary spacing

Preprocessing:

  1. Resampling spacing to 0.8 x 0.8 x 5mm
  2. Clipping intensity to [-1500, 500] HU
  3. Converting to channel first

Augmentation for training:

  1. Randomly cropping the volume to a fixed size 224 x 224 x 32
  2. Randomly applying spatial flipping
  3. Randomly applying spatial rotation
  4. Randomly shifting intensity of the volume

Output

Output: 2 channels

  • Label 0: everything else
  • Label 1: lung

Limitations

This training and inference pipeline was developed by NVIDIA. It is based on a segmentation model developed by NVIDIA researchers. This research use only software has not been cleared or approved by FDA or any regulatory agency. Clara pre-trained models are for developmental purposes only and cannot be used directly for clinical procedures.

References

[1] Liu, Siqi, et al. "3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes." In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 851-858). Springer, Cham. https://arxiv.org/pdf/1711.08580.pdf

License

End User License Agreement is included with the product. Licenses are also available along with the model application zip file. By pulling and using the Clara Train SDK container and downloading models, you accept the terms and conditions of these licenses.