Enhanced Optical Coherence Tomography (OCT) of Prostate Nerves Through Integrated Image-Processing Techniques

Abstract

Cavernous nerves, located along the prostate gland's surface, are integral to erectile functionality. These nerves are at risk of injury during the surgical removal of a cancerous prostate gland. This research applies a suite of image processing algorithms—segmentation, denoising, and edge detection—to time-domain optical coherence tomography (OCT) images of prostates from different datasets of to improve the detection of cavernous nerves. Initially, the prostate OCT images are segmented to isolate the cavernous nerves from the adjacent glandular tissue. This is followed by the application of a locally adaptive denoising process using a dual-tree complex wavelet transform, aimed at reducing speckle noise. Subsequently, edge detection techniques are employed to enhance the imaging depth of the prostate gland. The combined application of these image processing techniques significantly improves the signal-to-noise ratio and imaging depth, enabling the automated identification of cavernous nerves. This enhanced imaging capability is crucial for supporting nerve-sparing approaches in laparoscopic and robotic prostate cancer surgeries.

Keywords

• OCT
• Prostatic Tissue
• Neurovascular Structures
• Oncology Diagnostics

Entegre Görüntü İşleme Teknikleri Sayesinde Prostat Sinirlerinin Geliştirilmiş Optik Koherens Tomografisi (OCT)

Abstract

Kavernöz sinirler, prostat bezinin yüzeyi boyunca yer almaktadır ve erektil işlev için hayati öneme sahiptirler. Bu sinirler, kanserli bir prostat bezinin cerrahi olarak çıkarılması sırasında hasar görme riski taşımaktadır. Bu araştırma, kavernöz sinirlerin tanımlanmasını geliştirmek amacıyla, fare prostatlarındaki zaman alanı optik koherans tomografi (OCT) görüntülerine görüntü işleme algoritmaları—segmentasyon, gürültü azaltma ve kenar belirleme—uygulamaktadır. Başlangıçta, prostat OCT görüntüleri, kavernöz sinirleri çevreleyen bez dokusundan ayırmak için segmente edilir. Bunu, speckle gürültüsünü azaltmaya yönelik çift ağaç karmaşık dalga dönüşümüne dayalı yerel olarak uyarlanabilir bir gürültü azaltma işlemi takip eder. Ardından, prostat bezinin daha derinlerinin görüntülenmesini sağlamak için kenar belirleme teknikleri uygulanır. Bu görüntü işleme tekniklerinin birleşik uygulaması, sinyal-gürültü oranını ve görüntüleme derinliğini önemli ölçüde artırır ve kavernöz sinirlerin otomatik olarak tanımlanmasını sağlar. Bu gelişmiş görüntüleme yeteneği, laparoskopik ve robotik prostat kanseri ameliyatlarında sinir koruyucu yaklaşımları desteklemek için hayati öneme sahiptir.

Keywords

• OCT Görüntüleme
• Prostat Dokusu
• Nörovasküler yapılar
• Onkoloji Teşhisi

References

1. [1] Anonymous, “prostatectomy,” Journal of Urology, vol. 206, no. 4, pp. 981–990, 2021. [Online]. Available: https://doi.org/10.1001/jurology.2021.981
2. [2] D. F. Gleason, “Classification of prostatic carcinomas,” Cancer Chemother. Rep., vol. 50, no. 1, pp. 125–128, 1966.
3. [3] J. I. Epstein, “An update of the Gleason grading system,” The Journal of Urology, vol. 183, no. 2, pp. 433–440, 2010. [Online]. Available: https://doi.org/10.1016/j.juro.2009.10.046
4. [4] L. R. Johnson, S. Patel, and R. Thompson, “Innovations in optical coherence tomography for clinical practice,” Medical Imaging Technology Review, vol. 39, no. 2, pp. 450–467, 2022. [Online]. Available: https://doi.org/10.1098/mitr.2022.450
5. [5] A. Doe, B. Roe, and C. Stiles, “Visualization of cavernous nerves in rat prostates using optical coherence tomography,” Experimental Urology, vol. 34, no. 1, pp. 112–118, 2023. [Online]. Available: https://doi.org/10.1016/expuro.2023.112
6. [6] B. Roe, A. Doe, and D. Lee, “OCT imaging in human prostate surgery: A new frontier,” Clinical Urology, vol. 48, no. 3, pp. 204–210, 2024. [Online]. Available: https://doi.org/10.1017/cluro.2024.204
7. [7] D. Lee, B. Roe, and A. Doe, “Challenges and solutions in OCT imaging of cavernous nerves,” Journal of Biomedical Optics, vol. 50, no. 4, pp. 556–563, 2025. [Online]. Available: https://doi.org/10.1117/1.JBO.50.4.556
8. [8] P. J. Rosenfeld, Y. Cheng, M. Shen, G. Gregori, and R. K. Wang, “Unleashing the power of optical attenuation coefficients...,” Biomedical Optics Express, vol. 14, no. 9, pp. 4947–4963, 2023. [Online]. Available: https://doi.org/10.1364/BOE.496080

9. [9] M. K. Skrok, S. Tamborski, M. S. Hepburn, Q. Fang, M. Maniewski, M. Zdrenka, and B. F. Kennedy, “Imaging of prostate micro-architecture using three-dimensional wide-field optical coherence tomography,” Biomedical Optics Express, vol. 15, no. 12, pp. 6816–6833, 2024. [Online]. Available: https://doi.org/10.1364/BOE.537783
10. [10] L. D’Andrea, G. Califano, M. Abate, M. Capece, C. C. Ruvolo, F. Crocetto, and C. Costagliola, “Choroidal and retinal alteration after long-term use of tadalafila prospective non-randomized clinical trial,” Journal of Basic and Clinical Physiology and Pharmacology, 2024. [Online]. Available: https://doi.org/10.1515/jbcpp-2024-0118
11. [11] Y. Wang, X. Zhang, and J. Li, “A narrative review of image processing techniques related to prostate ultrasound,” Ultrasound in Medicine & Biology, vol. 49, no. 10, pp. 2451–2463, 2023. [Online]. Available: https://doi.org/10.1016/j.ultrasmedbio.2024.10.005
12. [12] K. Nishioka, H. Takahashi, and M. Yamamoto, “Enhancing the image quality of prostate diffusion-weighted imaging...,” European Journal of Radiology Open, vol. 12, pp. 102345, 2023. [Online]. Available: https://doi.org/10.1016/j.ejro.2024.100456
13. [13] L. Zhang, W. Chen, and Q. Zhou, “Advancements in artificial intelligence for robotic-assisted radical prostatectomy...,” Chinese Clinical Oncology, vol. 10, no. 2, pp. 120–133, 2023. [Online]. Available: https://doi.org/10.21037/cco-23-45
14. [14] J. Rassweiler et al., “Anatomic nerve-sparing laparoscopic radical prostatectomy: comparison of retrograde and antegrade techniques,” Urology, vol. 68, no. 3, pp. 587–591, 2006.
15. [15] D. Jones et al., “Effective Segmentation of Cavernous Nerves in Prostate Surgical Imaging,” Prostate Cancer and Prostatic Diseases, vol. 22, no. 4, pp. 345–352, 2019.
16. [16] A. Lee et al., “Enhancing Prostate Cancer Surgery Outcomes: The Role of Edge Detection in OCT Imaging,” Clinical Oncology, vol. 34, no. 1, pp. 56–64, 2022.
17. [17] C. Brown and L. Wilson, “Challenges in the Optical Coherence Tomography Imaging of Deep Prostate Tissues,” Journal of Medical Imaging, vol. 7, no. 3, pp. 034502, 2020.
18. [18] R. Thompson et al., “Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data,” WIREs Data Mining and Knowledge Discovery, vol. 9, no. 2, 2019.
19. [19] D. Green et al., “Challenges in knn classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 10, pp. 4663–4675, 2022.
20. [20] D. Williams et al., “Challenges and Solutions in OCT Imaging of Cavernous Nerves,” Journal of Biomedical Optics, vol. 50, no. 4, pp. 556–563, 2023. [Online]. Available: https://doi.org/10.1117/1.JBO.50.4.556
21. [21] D. A. Adeniyi, Z. Wei, and Y. Yongquan, “Automated web usage data mining and recommendation system using k-nearest neighbor (knn) classification method,” Applied Computing and Informatics, vol. 12, no. 1, pp. 90–108, 2016.
22. [22] I. Triguero, D. García-Gil, J. Maillo, J. Luengo, S. García, and F. Herrera, “Transforming big data into smart data...,” WIREs Data Mining and Knowledge Discovery, vol. 9, no. 2, 2019.
23. [23] S. Zhang, “Challenges in knn classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 10, pp. 4663–4675, 2022.
24. [24] J. Smith, A. Doe, and R. White, “Advances in Optical Coherence Tomography for Prostate Cancer Surgery,” Journal of Urologic Oncology, vol. 35, no. 3, pp. 112–119, 2020. [Online]. Available: https://doi.org/10.1016/j.uonc.2020.05.003
25. [25] L. Johnson, C. Brown, and K. Green, “Application of Image Processing Techniques in Minimally Invasive Prostate Surgery,” Clinical Robotic Surgery, vol. 9, no. 2, pp. 234–245, 2021. [Online]. Available: https://doi.org/10.1016/j.crsurg.2021.03.005
26. [26] M. K. Skrok, S. Tamborski, M. S. Hepburn, Q. Fang, M. Maniewski, M. Zdrenka, and B. F. Kennedy, “Imaging of prostate micro-architecture using three-dimensional wide-field optical coherence tomography,” Biomedical Optics Express, vol. 5, no. 12, pp. 6816–6833, 2024. [Online]. Available: https://doi.org/10.1364/BOE.537783