hristina-uzunova / TumorMassEffect

Pytorch implementation of the displacement model from "Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks".

TumorMassEffect

Pytorch implementation of the displacement model from "Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks".

Prerequisites

• Linux or MacOS
• Python 3
• PyTorch>0.4
• NVIDIA GPU + CUDA CuDNN

Datasets

Here we use simple threshhold-based shape-describing deformed and non-deformed versions of images. See Folder "images" for examples. The dataset-class is defined to process such images.

Citation

This work has been accepted to the MICCAI 2020. If you use this code, please cite as follows:

@inproceedings{MEGAN,
	title = {Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks},
	booktitle = {International Conference on  Medical Image Computing and Computer Assisted Intervention, MICCAI 2020},
	year = {In Press},
	author = {Uzunova, Hristina and Ehrhardt, Jan and Handels, Heinz}
}