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A sensitivity analysis for polyp segmentation with U-Net

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Abstract

Colorectal Cancer (CRC) is one of the most common cancer diseases in the world. Early diagnosis of the disease is of great importance for the recovery of the patient. Colonoscopy is the gold standard procedure used in the diagnosis of CRC. In this context, this study focused on the detection of polyps with high accuracy in order to contribute to the early diagnosis of CRC. Within the scope of the study, polyp segmentation was performed on the public CVC-Clinic DB polyp dataset. In the study, the basic U-Net model and its derivatives (modified U-Net, modified U-Net with transfer learning (VGG-16, VGG-19) in the encoding part) were used for the segmentation process. For sensitivity analysis, models were trained on three separate datasets prepared with different preprocessing methods in addition to the raw dataset with k-fold cross validations (k = 2,3,4) and different batch numbers (1,2,3,4,5) in each cross validation. As a result of the analysis, the best performance was obtained as 0.868, 0.799, 0.873 and 0.994 for Dice, Jaccard, Sensitivity, Specificity when the batch size was taken as 1 with fourfold cross validation in the modified U-Net trained with the Discrete Wavelet Transform (DWT) dataset. This model and its parameters were then tested with public datasets Kvasir-Seg and Etis-Larib Polyp DB. Moreover, different models were trained with the parameters of the most successful model. The results of all analyzes were interpreted and compared with the literature.

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Data availability

Publicly available datasets were analyzed in this study.

The CVC-Clinic DB datasets are publicly available here: https://polyp.grand-challenge.org/CVCClinicDB/ (accessed on 10 August 2022).

The ETIS-Larib dataset is publicly available here: https://polyp.grand-challenge.org/EtisLarib (accessed on 10 August 2022).

The Kvasir-SEG dataset is publicly available here: https://datasets.simula.no/kvasir-seg/ (accessed on 10 August 2022).

References

  1. Abraham N, Khan NM A novel focal tversky loss function with improved attention u-net for lesion segmentation. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), 2019. IEEE, pp 683–687

  2. Ahmed N, Natarajan T, Rao KR (1974) Discrete Cosine Transform. IEEE Trans Comput C–23(1):90–93. https://doi.org/10.1109/T-C.1974.223784

    Article  MathSciNet  MATH  Google Scholar 

  3. Arnold M, Ghosh A, Ameling S, Lacey G (2010) Automatic segmentation and inpainting of specular highlights for endoscopic imaging. EURASIP J Image Video Process 2010:1–12

    Article  Google Scholar 

  4. Ashkani Chenarlogh V, Shabanzadeh A, Ghelich Oghli M, Sirjani N, Farzin Moghadam S, Akhavan A, Arabi H, Shiri I, Shabanzadeh Z, Sanei Taheri M (2022) Clinical target segmentation using a novel deep neural network: double attention Res-U-Net. Sci Rep 12(1):6717

    Article  Google Scholar 

  5. Banik D, Bhattacharjee D, Nasipuri M (2020) A multi-scale patch-based deep learning system for polyp segmentation. In: Advanced Computing and Systems for Security. Springer 12:109–119

  6. Banik D, Roy K, Bhattacharjee D, Nasipuri M, Krejcar O (2020) Polyp-Net: A multimodel fusion network for polyp segmentation. IEEE Trans Instrum Meas 70:1–12

    Article  Google Scholar 

  7. Bardhi O, Sierra-Sosa D, Garcia-Zapirain B, Elmaghraby A (2017) Automatic colon polyp detection using Convolutional encoder-decoder model. In: IEEE international symposium on signal processing and information technology (ISSPIT). IEEE, pp 445–448. https://doi.org/10.1109/ISSPIT.2017.8388684

  8. Bernal J, Sánchez FJ, Fernández-Esparrach G, Gil D, Rodríguez C, Vilariño F (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99–111. https://doi.org/10.1016/j.compmedimag.2015.02.007

    Article  Google Scholar 

  9. Bernal J, Sánchez J, Vilarino F (2012) Towards automatic polyp detection with a polyp appearance model. Pattern Recogn 45(9):3166–3182

    Article  Google Scholar 

  10. Bernal J, Tajkbaksh N, Sánchez FJ, Matuszewski BJ, Chen H, Yu L, Angermann Q, Romain O, Rustad B, Balasingham I (2017) Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge. IEEE Trans Med Imaging 36(6):1231–1249

    Article  Google Scholar 

  11. Brandao P, Mazomenos E, Ciuti G, Caliò R, Bianchi F, Menciassi A, Dario P, Koulaouzidis A, Arezzo A, Stoyanov D (2017) Fully convolutional neural networks for polyp segmentation in colonoscopy. In: Medical Imaging: Computer-Aided Diagnosis. Spie 10134:101–107

  12. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. International conference on medical image computing and computer-assisted intervention. Springer, pp 424–432

    Google Scholar 

  13. Hu Y, Li Q, Ma S, Kuo C-C A VLSI architecture for a fast computation of the 2-D discrete wavelet transform. In: 2007 IEEE International Symposium on Circuits and Systems (ISCAS), 2007. IEEE, pp 3980–3983

  14. Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863

    Article  Google Scholar 

  15. Jha D, Smedsrud PH, Riegler MA, Halvorsen P, De Lange T, Johansen D, Johansen HD (2020) Kvasir-seg: A segmented polyp dataset. In: International Conference on Multimedia Modeling, Springer, pp 451-4625

  16. Kang J, Gwak J (2019) Ensemble of instance segmentation models for polyp segmentation in colonoscopy images. IEEE Access 7:26440–26447

    Article  Google Scholar 

  17. Kolligs FT (2016) Diagnostics and Epidemiology of Colorectal Cancer. Visc Med 32(3):158–164. https://doi.org/10.1159/000446488

    Article  Google Scholar 

  18. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25

  19. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  20. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  21. Li Q, Yang G, Chen Z, Huang B, Chen L, Xu D, Zhou X, Zhong S, Zhang H, Wang T (2017) Colorectal polyp segmentation using a fully convolutional neural network. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017: 1–5. https://doi.org/10.1109/CISP-BMEI.2017.8301980

  22. Lin T-Y, Goyal P, Girshick R, He K, Dollár P Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, 2017. pp 2980–2988

  23. Nguyen Q, Lee S-W Colorectal segmentation using multiple encoder-decoder network in colonoscopy images. In: 2018 IEEE first international conference on artificial intelligence and knowledge engineering (AIKE), 2018. IEEE, pp 208–211

  24. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999

  25. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

  26. Schmeelk J (2002) Wavelet transforms on two-dimensional images. Math Comput Model 36(7):939–948. https://doi.org/10.1016/S0895-7177(02)00238-8

    Article  MathSciNet  MATH  Google Scholar 

  27. Shorten C, Khoshgoftaar TM (2019) A survey on Image Data Augmentation for Deep Learning. J Big Data 6(1):60. https://doi.org/10.1186/s40537-019-0197-0

    Article  Google Scholar 

  28. Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 9(2):283–293. https://doi.org/10.1007/s11548-013-0926-3

    Article  Google Scholar 

  29. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556

  30. Society AC (2021) Cancer Facts & Figures 2021. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2021/cancer-facts-and-figures-2021.pdf. Accessed 30 Aug 2022

  31. Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J Roy Stat Soc: Ser B (Methodol) 36(2):111–133

    MathSciNet  MATH  Google Scholar 

  32. Williams CB (2009) Insertion Technique. In: Colonoscopy. pp 535–559. https://doi.org/10.1002/9781444316902.ch40

  33. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop Proceedings 4. Springer pp 3–11

  34. Zuiderveld K (1994) VIII.5. - Contrast Limited Adaptive Histogram Equalization. In: Heckbert PS (ed) Graphics Gems. Academic Press, pp 474–485. https://doi.org/10.1016/B978-0-12-336156-1.50061-6

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  1. Konya Technical University, Konya, Turkey

    Ahmet Solak & Rahime Ceylan

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  1. Ahmet Solak
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Solak, A., Ceylan, R. A sensitivity analysis for polyp segmentation with U-Net. Multimed Tools Appl 82, 34199–34227 (2023). https://doi.org/10.1007/s11042-023-16368-9

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