Hippocampal segmentation using the UNet network for each axis

Juan Carlos Aguirre Arango

Last update: Sep 2, 2021

Overview

Hipposeg

Hippocampal segmentation using the UNet network for each axis, inspired by https://github.com/MICLab-Unicamp/e2dhipseg

Red: False Positive
Green: True Positive
Blue: False Negative

Install

Clone the repository

git clone https://github.com/aguirrejuan/hipposeg.git

Install requeriments

pip install -r requirements.txt

Inference

Download Pretrained Weights

Download the Weigths trained with ADNI HarP Dataset and put in the model directory.

Run the code

python inference.py  --path_vol "example.nii"

The result will be in the current directory

Train

Download the VGG11 model

Download the VGG11 model and put in the model directory.

Run the code

python train.py  --train_path "directory_mris/"  \
                  --train_paht_label "directory_mris_labels/" \
                  --epochs number_of_epochs

Evaluate the Model

python  evaluate.py --path_data "directory_mris/" \
                    --path_data_label "directory_mris_labels/"

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Comments

How can I access the ADNI HarP dataset?

Hi, Im new to this. Kindly, please assist me. The weights are downloaded using the given link but how can I access ADNI harp dataset where NIFTI files are kept?

opened by nimra008 0

Owner

Juan Carlos Aguirre Arango

Electronic Engineer

GitHub

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Hippocampal segmentation using the UNet network for each axis

Related tags

Overview

Hipposeg

Install

Clone the repository

Install requeriments

Inference

Download Pretrained Weights

Run the code

Train

Download the VGG11 model

Run the code

Evaluate the Model

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Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

This is a file about Unet implemented in Pytorch

unet-family: Ultimate version

3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021)

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Comments

How can I access the ADNI HarP dataset?

Owner

Juan Carlos Aguirre Arango

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Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

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