REFERENCES
1. Siegel RL, Miller KD, Sauer AG, et al. Colorectal cancer statistics, 2020. CA Cancer J Clin 2020;70:145-64.
2. American Cancer Society. Colorectal cancer facts & figures 2017–2019. Available from: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/colorectal-cancer-facts-and-figures/colorectal-cancer-facts-and-figures-2017-2019.pdf. [Last accessed on 25 Apr 2024].
3. Zhao Q, Meng MQH. Polyp detection in wireless capsule endoscopy images using novel color texture features. In: 2011 9th World Congress on Intelligent Control and Automation; 2011 Jun 21-25; Taipei, Taiwan, China. IEEE; 2011. pp. 948-52.
4. Li B, Meng MQH. Automatic polyp detection for wireless capsule endoscopy images. Expert Syst Appl 2012;39:10952-58.
5. Mamonov AV, Figueiredo IN, Figueiredo PN, Tsai YHR. Automated polyp detection in colon capsule endoscopy. IEEE Trans Med Imaging 2014;33:1488-502.
6. Silva J, Histace A, Romain O, Dray X, Granado B. Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int J Comput Assist Radiol Surg 2014;9:283-93.
7. Iwahori Y, Shinohara T, Hattori A, et al. Automatic polyp detection in endoscope images using a hessian filter. In: MVA2013 IAPR International Conference on Machine Vision Applications; 2013 May 20-23; Kyoto, Japan. 2013. pp. 21-4. Available from: https://www.mva-org.jp/Proceedings/2013USB/papers/03-01.pdf. [Last accessed on 25 Apr 2024].
8. Jia X, Mai X, Cui Y, et al. Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans Autom Sci Eng 2020;17:1570-84.
9. Yuan Y, Meng MQH. Deep learning for polyp recognition in wireless capsule endoscopy images. Med Phys 2017;44:1379-89.
10. Yuan Y, Qin W, Ibragimov B, et al. Densely connected neural network with unbalanced discriminant and category sensitive constraints for polyp recognition. IEEE Trans Autom Sci Eng 2019;17:574-83.
11. Jia X, Xing X, Yuan Y, Xing L, Meng MQH. Wireless capsule endoscopy: a new tool for cancer screening in the colon with deep-learning-based polyp recognition. Proc IEEE 2019;108:178-97.
12. Wu H, Zhao Z, Wang Z. META-Unet: multi-scale efficient transformer attention Unet for fast and high-accuracy polyp segmentation. IEEE Trans Autom Sci Eng 2023:1-12.
13. Liu G, Chen Z, Liu D, Chang B, Dou Z. FTMF-Net: a fourier transform-multiscale feature fusion network for segmentation of small polyp objects. IEEE Trans Instrum Meas 2023;72:1-15.
14. Ta N, Chen H, Lyu Y, Wu T. Ble-net: boundary learning and enhancement network for polyp segmentation. Multimed Syst 2023;29:3041-54.
15. Zhou SK, Rueckert D, Fichtinger G. Handbook of medical image computing and computer assisted intervention Elsevier; 2020.
16. Zhou SK, Greenspan H, Davatzikos C, et al. A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc IEEE Inst Electr Electron Eng 2021;109:820-38.
17. Lange T, Papenberg N, Heldmann S, et al. 3D ultrasound-CT registration of the liver using combined landmark-intensity information. Int J Comput Assist Radiol Surg 2009;4:79-88.
18. Ibragimov B, Korez R, Likar B, Pernuš F, Xing L, Vrtovec T. Segmentation of pathological structures by landmark-assisted deformable models. IEEE Trans Med Imaging 2017;36:1457-69.
19. Chiras J, Depriester C, Weill A, Sola-Martinez MT, Deramond H.[Percutaneous vertebral surgery. Technics and indications]. J Neuroradiol 1997;24: 45-59. Available from: https://pubmed.ncbi.nlm.nih.gov/9303944/. [Last accessed on 25 Apr 2024].
20. Liu D, Zhou KS, Bernhardt D, Comaniciu D. Search strategies for multiple landmark detection by submodular maximization. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2010 Jun 13-18; San Francisco, CA, USA. IEEE; 2010. pp. 2831-38.
21. Lindner C, Bromiley PA, Ionita MC, Cootes TF. Robust and accurate shape model matching using random forest regression-voting. IEEE Trans Pattern Anal Mach Intell 2014;37:1862-74.
22. Ebner T, Stern D, Donner R, Bischof H, Urschler M. Towards automatic bone age estimation from MRI: localization of 3D anatomical landmarks. Med Image Comput Comput Assist Interv 2014;17:421-8.
23. Oktay O, Bai W, Guerrero R, et al. Stratified decision forests for accurate anatomical landmark localization in cardiac images. IEEE Trans Med Imaging 2016;36:332-42.
24. Wester BH. Detecting anatomical landmarks in 3D cardiovascular images using convolutional neural network. 2020. Available from: https://www.duo.uio.no/handle/10852/80720. [Last accessed on 25 Apr 2024].
25. Song Y, Qiao X, Iwamoto Y, Chen Y. Automatic cephalometric landmark detection on X-ray images using a deep-learning method. Appl Sci 2020;10:2547.
26. Leroy G, Rueckert D, Alansary A. Communicative reinforcement learning agents for landmark detection in brain images. In: MLCN 2020, RNO-AI 2020: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. Cham: Springer; 2020. pp. 177-86.
27. Lian C, Wang F, Deng HH, et al. Multi-task dynamic transformer network for concurrent bone segmentation and large-scale landmark localization with dental CBCT. Med Image Comput Comput Assist Interv 2020;12264:807-16.
28. Zhu H, Yao Q, Xiao L, Zhou SK. You only learn once: universal anatomical landmark detection. In: MICCAI 2021: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Cham: Springer; 2021. pp. 85-95.
29. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR); 2016 Jun 27-30; Las Vegas, NV, USA. IEEE; 2016. pp. 770-78.
30. Kohn N, Fuchtmann J, Morandell J, Ostler D, Wilhelm D, Feußner H. Markerless endoluminal navigation (Project BIOPASS) - Deep learning based detection of intestinal segments for colorectal endoscopic investigations. In: Conference Proceedings: 17th Annual Meeting of the German Society for Computer- and Robot-Assisted Surgery e.V. 2018. pp. 223-25. (in German) Available from: https://www.curac.org/images/advportfoliopro/images/CURAC2018/CURAC%202018%20Tagungsband.pdf. [Last accessed on 25 Apr 2024].
31. Horsch A, Allescher HD. Automatische lokalisationserkennung in der endoskopie des gastrointestinaltrakts - eine Machbarkeitsstudie. In: Meiler M, Saupe D, Kruggel F, Handels H, Lehmann TM, editors. Bildverarbeitung für die Medizin 2002. Berlin: Springer; 2002. pp. 163-66.
32. Northern Care Alliance NHS Foundation Trust. Gastroenterology. Available from: https://www.ncaresearch.org.uk/research/gastroenterology/. [Last accessed on 25 Apr 2024].
