ColonMapper: topological mapping and localization for colonoscopies

ColonMapper is a topological mapping and localization algorithm able to build a topological map of the whole colon autonomously. In a second stage, ColonMapper can localize another sequence of the same patient against the previously built topological map.

Installation

To install the ColonMapper environment, simply use conda as:

conda create -n colonmapper --file requirements.txt

Paths are defined in settings.py. There you should define where you store the data and the evaluation logs. Folder structure should resemble this:

.
├── datasets     # endomapper and C3VD
├── models       # trained models to run experiments
└── logs         # where results are stored
    └── model 1  # example model
      ├── experiment 1   
      └── experiment 2  

Download trained models and evaluation data

Before starting, you have to download the trained models, which can be found here.

The images used for evaluation, both for Endomapper and C3VD, can be found here.

Usage

To run the topological mapping as described in the paper, run the following command:

cd ColonMapper
python mapping_vg.py --resume=[PATH_TO_MODELS]/resnet50conv4_netvlad_0_0_640_hard_resize_layer2/best_model.pth --datasets 027 035 cross C3VD

To run the Bayesian localization against canonical maps as described in the paper, run the following command:

cd ColonMapper
python localization_vg.py --experiment_name=bayesian_reject --resume=[PATH_TO_MODELS]/resnet50conv4_netvlad_0_0_640_hard_resize_layer2/best_model.pth --datasets 027 entry_cross cross C3VD --bayesian --threshold_probability=0.5 --reject_outliers --reject_strategy=diffusion

Related Publication:

Javier Morlana, Juan D. Tardós and J.M.M. Montiel, ColonMapper: topological mapping and localization for colonoscopies, ICRA 2024. PDF

@inproceedings{morlana2024colonmapper,
  title={ColonMapper: topological mapping and localization for colonoscopy},
  author={Morlana, Javier and Tard{\'o}s, Juan D and Montiel, JMM},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={6329--6336},
  year={2024},
  organization={IEEE}
}