The repo of our paper 'Deep Closing: Enhancing Topological Connectivity in Medical Tubular Segmentation'

conda create -n DeepClosing python=3.10
conda activate DeepClosing
pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
pip install pytorch-lightning==1.5.0
pip install monai==0.9.0
pip install scikit-image
pip install wandb
pip install nibabel

To begin with, the proposed framework,Deep Closing, consists of two operation:

• (1) Deep Dilation:

  DeepClosing/DeepClosing.py

  Line 289 in c7c5ce9

  def DeepDilation(self,T,is_infer_sliding_window=True, sw_roi_size=(224,224),sw_batch_size=4,verbose=False):

• (2) Simple Component Erosion:

  DeepClosing/DeepClosing.py

  Line 259 in 2e7cb06

  def Simple_Component_Erosion(self, T, M_T):

• (*) DeepClosing = DeepDilation + Simple Component Erosion (Inference):

  DeepClosing/DeepClosing.py

  Line 313 in 2e7cb06

  def DeepClosing(self,T,is_infer_sliding_window=True, sw_roi_size=(224,224),sw_batch_size=4,verbose=False):

The implementation of the proposed Simple Point Erosion Module is presented in the position below:

Besides, the Masked Shape Reconstruction (Training Stage) is presented in the position below:

We plan to provide more detailed information after the acceptance of our paper. Thanks for your constructive comments to help us improve our paper.