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Notebook for Medical Image Segmentation

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Description

3D-UNet medical image segmentation allows for seamless segmentation of 3D volumes, with high accuracy and performance.

Publisher

NVIDIA

Use Case

Other

Framework

Other

Latest Version

final

Modified

July 14, 2021

Compressed Size

361.16 KB

Model Review

3D-UNet allows for seamless segmentation of 3D volumes, with high accuracy and performance, and can be adapted to solve many different segmentation problems. In this resource we use a 3D-UNet model for brain tumor segmentation.

3D-UNet consists of a contractive and expanding path. It repeatedly applies unpadded convolutions followed by max pooling for downsampling. Every step in the expanding path consists of an upsampling of the feature maps and a concatenation with the correspondingly cropped feature map from the contractive path.

Requirements

This repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies.

Aside from these dependencies, ensure you have the following components:

  • NVIDIA Docker
  • TensorFlow 20.06-tf1-py3 NGC container
  • GPU-based architecture:
    • NVIDIA Volta
    • NVIDIA Turing
    • NVIDIA Ampere architecture

To train your model using mixed or TF32 precision with Tensor Cores or using FP32, follow the quick start guide steps and run the model using the default parameters of the 3D-UNet model on the Brain Tumor Segmentation 2019 dataset.