Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach
Abstract
Colorectal Cancer (CRC) is a major contributor to cancer-related mortality worldwide, necessitating early detection and treatment of polyps to prevent cancer progression. A colonoscopy is a critical diagnostic procedure for identifying colon abnormalities and removing premalignant polyps. However, accurately segmenting polyps in colonoscopy images poses challenges due to their diverse appearance and indistinct boundaries. In this study, we investigate augmentation techniques to enhance polyp semantic segmentation using the U-Net model. Our analysis reveals that the most effective technique is found in sub-scenario 2.6.c with an input size of 320×320, striking a favorable balance between accuracy and efficiency. Additionally, we explore the benefits of larger input sizes, taking into account resource considerations. Moreover, we conduct further testing of the best augmentation technique identified in previous experiments with the SegNet model. The results show a 3.5% improvement in the dice coefficient and slightly better qualitative outcomes. However, it is important to note that this enhancement comes with a nearly fivefold increase in training time. Moving forward, our objective is to develop a unified model for segmenting diverse medical images, pushing the boundaries of polyp detection and medical imaging. This research provides valuable insights and lays the foundation for more advanced applications in polyp detection and medical image analysis.
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References
R. L. Siegel et al., “Colorectal cancer statistics, 2020,” CA Cancer J Clin, vol. 70, no. 3, pp. 145–164, May 2020, doi: 10.3322/caac.21601.
D. Jha et al., “Kvasir-SEG: A Segmented Polyp Dataset,” 2020, pp. 451–462. doi: 10.1007/978-3-030-37734-2_37.
D. A. Lieberman et al., “Use of Colonoscopy to Screen Asymptomatic Adults for Colorectal Cancer,” New England Journal of Medicine, vol. 343, no. 3, pp. 162–168, Jul. 2000, doi: 10.1056/NEJM200007203430301.
P. F. Pinsky and R. E. Schoen, “Contribution of Surveillance Colonoscopy to Colorectal Cancer Prevention,” Clinical Gastroenterology and Hepatology, vol. 18, no. 13, pp. 2937-2944.e1, Dec. 2020, doi: 10.1016/j.cgh.2020.01.037.
D.-P. Fan et al., “PraNet: Parallel Reverse Attention Network for Polyp Segmentation,” 2020, pp. 263–273. doi: 10.1007/978-3-030-59725-2_26.
S. N. Hong et al., “The Effect of the Bowel Preparation Status on the Risk of Missing Polyp and Adenoma during Screening Colonoscopy: A Tandem Colonoscopic Study,” Clin Endosc, vol. 45, no. 4, p. 404, 2012, doi: 10.5946/ce.2012.45.4.404.
A. Leufkens, M. van Oijen, F. Vleggaar, and P. Siersema, “Factors influencing the miss rate of polyps in a back-to-back colonoscopy study,” Endoscopy, vol. 44, no. 05, pp. 470–475, May 2012, doi: 10.1055/s-0031-1291666.
S. B. Ahn, D. S. Han, J. H. Bae, T. J. Byun, J. P. Kim, and C. S. Eun, “The Miss Rate for Colorectal Adenoma Determined by Quality-Adjusted, Back-to-Back Colonoscopies,” Gut Liver, vol. 6, no. 1, pp. 64–70, Jan. 2012, doi: 10.5009/gnl.2012.6.1.64.
S. Ameling, S. Wirth, D. Paulus, G. Lacey, and F. Vilarino, “Texture-Based Polyp Detection in Colonoscopy,” 2009, pp. 346–350. doi: 10.1007/978-3-540-93860-6_70.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” 2015, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.
D. Jha, M. A. Riegler, D. Johansen, P. Halvorsen, and H. D. Johansen, “DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation,” in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), IEEE, Jul. 2020, pp. 558–564. doi: 10.1109/CBMS49503.2020.00111.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans Pattern Anal Mach Intell, vol. 39, no. 12, pp. 2481–2495, Dec. 2017, doi: 10.1109/TPAMI.2016.2644615.
D. Lin, Y. Li, T. L. Nwe, S. Dong, and Z. M. Oo, “RefineU-Net: Improved U-Net with progressive global feedbacks and residual attention guided local refinement for medical image segmentation,” Pattern Recognit Lett, vol. 138, pp. 267–275, Oct. 2020, doi: 10.1016/j.patrec.2020.07.013.
K. Pogorelov et al., “Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection,” in Proceedings of the 8th ACM on Multimedia Systems Conference, 2017, pp. 164–169.
B. Murugesan, K. Sarveswaran, S. M. Shankaranarayana, K. Ram, J. Joseph, and M. Sivaprakasam, “Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Jul. 2019, pp. 7223–7226. doi: 10.1109/EMBC.2019.8857339.
H. Toda et al., “Shape Recovery of Polyp Using Blood Vessel Detection and Matching Estimation by U-Net,” in 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI), IEEE, Jul. 2019, pp. 450–453. doi: 10.1109/IIAI-AAI.2019.00098.
N. Ibtehaz and M. S. Rahman, “MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation,” Neural Networks, vol. 121, pp. 74–87, Jan. 2020, doi: 10.1016/j.neunet.2019.08.025.
B. Ji et al., “A multi-scale recurrent fully convolution neural network for laryngeal leukoplakia segmentation,” Biomed Signal Process Control, vol. 59, p. 101913, May 2020, doi: 10.1016/j.bspc.2020.101913.
S. M. Kamrul Hasan and C. A. Linte, “U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instruments from Laparoscopic Images,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Jul. 2019, pp. 7205–7211. doi: 10.1109/EMBC.2019.8856791.
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