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deepatlas's Introduction

DeepAtlas

This is the repository for the paper "DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation " at MICCAI 2019 [arxiv] by Zhenlin Xu and Marc Niethammer.

Install torch>=1.0 and torchvision according to your config Install other dependencies with pip install -r requirements.txt

Data

The OAI knee MRIs and MindBoggle101 brain MRIs were used in the paper. We can only share our processed brain MRIs due to copyright limitations. Please download the preprocessed Mindboggle101 from Google Drive. Note that, in DeepAtlas paper, a few images were disregarded due the segmentation labeling errors that were fixed in the later versions of MB101.

Train a segmentation model with MindBoggle101 data

E.g. Train a segmentation model with 21 training samples python train_seg.py --num-samples 21 --data-root $DATA_ROOT --num-epochs 100 --lr 1e-3 --log-root ./logs

Train a registration model with MindBoggle101 data

TODO

Train DeepAtlas model with MindBoggle101 data

TODO

deepatlas's People

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deepatlas's Issues

joint training

Very much looking forward to the authors sharing the code for the joint training part soon.

size problem

当我使用我的数据集在你的网络架构复现时,出现size错误,请问这是不是预处理时候的问题。
期待你的回复!
陈勇
image

The code loss function of the segmentation part does not match the paper

The loss function of the segmentation only uses the output and the label to measure, does not use the registration information. The segmentation information that appears in the registration code is also confusing.

Recently, I am paying attention to the direction of joint segmentation and registration, I have learned a lot from your paper.
Thank you for your open source.

is code completed?

Hi,

I find you paper is interesting and enlightening, and I want to follow the paper.

So, I wonder whether your code is completed?

Thank you

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