Abstract
Colorectal cancer cases have been increasing at an alarming rate each year, imposing a healthcare burden worldwide. Multiple efforts have been made to treat this malignancy. However, early screening has been the most promising solution. Optical endoscopy is the primary diagnosis and treatment tool for these malignancies. Even though its success, the endoscopic process represents a challenge due to noisy data, a limited field of view and the presence of multiple artefacts. In this work, we present a comparison between two real-time deep learning frameworks trained to detect artefacts in endoscopic data. Both networks are trained using different data augmentation techniques to analyze their effect when the models are evaluated using data coming from a different distribution. We evaluated these models using the mean average precision (mAP) evaluation metric at a different intersection over union values. Both models outperformed state-of-the-art methods that were evaluated using the same dataset. Also, the use of data augmentation techniques showed an overall improvement in terms of mAP when compared to the case in which no augmentation was applied.
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References
Ali, S., et al.: Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Anal. 70, 102002 (2021). https://doi.org/10.1016/j.media.2021.102002arXiv:2010.06034
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection (2020). https://doi.org/10.48550/ARXIV.2004.10934arxiv:2004.10934
Bodenstedt, S., et al.: Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery (2018)
Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: real-time instance segmentation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9156–9165. IEEE, Seoul, Korea (South), October 2019. https://doi.org/10.1109/ICCV.2019.00925, https://ieeexplore.ieee.org/document/9010373/
Casado-García, Á., Heras, J.: Ensemble methods for object detection. In: ECAI (2020)
Chen, H., Lian, C., Wang, L.: Endoscopy artefact detection and segmentation using deep convolutional neural network (2020)
Elgendy, M.: Deep Learning for Vision Systems. Manning Publications Co, Shelter Island, NY (2020)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). https://doi.org/10.48550/ARXIV.1704.04861, arxiv:1704.04861
Jadhav, S., Bamba, U., Chavan, A., Tiwari, R., Raj, A.: Multi-plateau ensemble for endoscopic artefact segmentation and detection, March 2020
Kirthika, N., Sargunam, B.: YOLOV4 for multi-class artefact detection in endoscopic images, pp. 73–77, May 2021. https://doi.org/10.1109/ICSPC51351.2021.9451761, https://ieeexplore.ieee.org/document/9451761/
Kohli, D.R., Baillie, J.: How Endoscopes Work (2019)
Maier-Hein, L., et al.: Heidelberg colorectal data set for surgical data science in the sensor operating room. Sci. Data 8, 101 (2021). https://doi.org/10.1038/s41597-021-00882-2
Münzer, B., Schoeffmann, K., Böszörmenyi, L.: Content-based processing and analysis of endoscopic images and videos: a survey. Multimedia Tools Appl. 77(1), 1323–1362 (2017). https://doi.org/10.1007/s11042-016-4219-z
Polat G., Sen D., Inci, A., Temizel, A.: Endoscopic artefact detection with ensemble of deep neural networks and false positive elimination. In: CEUR Workshop (2020)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. arXiv:1506.02640 [cs], May 2016
Tang, Y., Anandasabapathy, S., Richards-Kortum, R.: Advances in optical gastrointestinal endoscopy: a technical review. Mol. Oncol. 1878–0261.12792 (2020). https://doi.org/10.1002/1878-0261.12792
Ward, T.M., et al.: Computer vision in surgery. Surgery 169(5), 1253–1256 (2021). https://doi.org/10.1016/j.surg.2020.10.039
WHO: https://gco.iarc.fr/
Xu, Z., Ali, S., Gupta, S., Celik, N., Rittscher, J.: Improved artifact detection in endoscopy imaging through profile pruning. In: Papież, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds.) Medical Image Understanding and Analysis. p. 87–97. Lecture Notes in Computer Science, Springer International Publishing, Cham (2021). DOI: https://doi.org/10.1007/978-3-030-80432-9_7
Zhang, Y., Xie, D.: J. Zhejiang Univ.-Sci. B 20(12), 1014–1020 (2019). https://doi.org/10.1631/jzus.B1900340
Acknowledgments
The authors wish to thank the AI Hub and the CIIOT at ITESM for their support for carrying the experiments reported in this paper in their NVIDIA’s DGX computer.
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Chavarrias-Solano, P.E., Ali-Teevno, M., Ochoa-Ruiz, G., Ali, S. (2022). Improving Artifact Detection in Endoscopic Video Frames Using Deep Learning Techniques. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13612. Springer, Cham. https://doi.org/10.1007/978-3-031-19493-1_26
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