Awesome
Sinus-Surgery-Endoscopic-Image-Datasets
Novel image segmentation datasets collected from endoscopic videos of sinus surgery processes
Examples
UW Sinus Surgery Cadaver/Live (UW-Sinus-Surgery-C/L) Dataset:
Link: https://digital.lib.washington.edu/researchworks/handle/1773/45396
This dataset was developed at the University of Washington's BioRobotics Lab (http://brl.ee.washington.edu). It has endoscopic sinus surgery images with manual annotations for surgical instrument segmentation task. The dataset was collected from endoscopic sinus surgeries performed by surgeons, which are featured by dexterous tip motion, narrow operation space and close lens-object distance. More details can be found in the references [1,2].
UW-Sinus-Surgery-C/L consists of two sub-datasets: cadaver surgery dataset (UW-Sinus-Surgery-C) and live surgery dataset (UW-Sinus-Surgery-L). In each dataset, the folder "images" has endoscopic images and the folder "labels" has the segmentation ground truths (0 stands for background and 1 for surgical instrument)
The dataset folder is arranged as follows:
|--- readme.txt
|--- cadaver
|---images
|---labels
|
|—— live
|---images
|---labels
Image naming:
i) Sinus-Surgery-C dataset: S[video_ID]_[frame_index]
ii) Sinus-Surgery-L dataset: L[video_ID]_[frame_index]
[video_ID] shows from which video the image was extracted, [frame_index] is the frame's index in the corresponding video.
3-fold cross-validation experimental setup:
In [2], the image segmentation performances were evaluated based on K-fold cross-validation method, to avoid the bias caused by dataset split. The 3-fold crossvalidation is conducted with the setup below:
i) Sinus-Surgery-C dataset: fold 1: 1st~4th videos; fold 2: 5th-7th videos; fold 3: 8th-10th videos
ii) Sinus-Surgery-L dataset: fold 1: 1st video; fold 2: 2nd video; fold 3: 3rd video
If you are trying to follow the experiments in [2], please contact {fbqin 'at' uw.edu} or {qinfangbo2013 'at' ia.ac.cn} to request the datasets which had already been organized to 3-fold.
Contact
For any questions and suggestions, please contact us via shl102@eng.ucsd.edu and qinfangbo2013@ia.ac.cn.
If you find this dataset helpful in your research, please cite our papers [1] and [2].
References
[1] S. Lin, F. Qin, et al., “LC-GAN: Image-to-image translation based on generative adversarial network for endoscopic images,” arXiv preprint arXiv:2003.04949, 2020. (accepted by IROS 2020)
[2] F. Qin, S. Lin, et al., “Towards better surgical instrument segmentation in endoscopic vision: multi-angle feature aggregation and contour supervision,” IEEE Robotics and Automation Letters, 2020 (IROS2020 presentation), https://ieeexplore.ieee.org/document/9140341
[3] S. Lin, F. Qin, R. A. Bly, et al., "Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video," in Proc. Int. Workshop Multiscale Multimodal Med. Imag., pp.93-100, 2019.
[4] S. Lin, X. Gu, R. A. Bly, et al., "Video-based automatic and objective endoscopic sinus surgery skill assessment," in Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling., vol. 11315, p. 113152L, 2020.