This is the Pytorch implementation of ISINet: An Instance-Based Approach for Surgical Instrument Segmentation published at MICCAI2020.
Requirements:
- Python >= 3.6
- Pytorch == 1.4
- numpy
- scikit-image
- tqdm
- scipy == 1.1
- flownet2
- Detectron v.1 (for using our pre-trained weights)
Pre-trained weights are publicly available on the project page.
Additional annotations for the EndoVis 2018 Dataset are publicly available on the project page.
Check the instructions detailed in data/README.md
python -W ignore main.py --inference --model FlowNet2 --batch_size batch_size --number_workers num_workers \
--inference_dataset RobotsegTrackerDataset \ --inference_dataset_img_dir /path/to/images \ --inference_batch_size batch_size \
--inference_dataset_coco_ann_path /path/to/coco/annotations/file.json \
--inference_dataset_segm_path /path/to/mask-rcnn/inference/segm.json \
--inference_dataset_ann_dir /path/to/annotations \
--inference_dataset_cand_dir /path/to/save/candidates \ --inference_dataset_nms 'True' \
--save /path/to/save/predictions \
--inference_dataset_dataset '2017' or '2018' \
--inference_dataset_maskrcnn_inference 'False' \
--assignment_strategy 'weighted_mode' \ --inference_dataset_prev_frames 7 \
--threshold 0.0 for 2017 and 0.5 for 2018 \
--resume /path/to/flownet/checkpoint --num-classes number_of_classes
If you found our work useful in your research, please use the following BibTeX entry for citation:
@article{ISINet2020,
title={ISINet: An Instance-Based Approach for Surgical Instrument Segmentation},
author={Cristina Gonz{\'a}lez and Laura Bravo-S{\'a}nchez and Pablo Arbelaez},
journal={arXiv preprint arXiv:2007.05533},
year={2020}
}
Our code is build upon FlowNet2, we thank the authors for their contributions to the community.
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