LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,237
تعداد مقالات11,268
تعداد مشاهده مقاله80,144,263
تعداد دریافت فایل اصل مقاله116,525,090
Mofid, Bahram, Mosalla, Sayed Mohammad Modarres, Goodarzi, Masumeh, Tavakoli, Hassan. (1404). Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy. سامانه مدیریت نشریات علمی, 15(6), 575-588. doi: 10.31661/jbpe.v0i0.2307-1649
Bahram Mofid; Sayed Mohammad Modarres Mosalla; Masumeh Goodarzi; Hassan Tavakoli. "Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy". سامانه مدیریت نشریات علمی, 15, 6, 1404, 575-588. doi: 10.31661/jbpe.v0i0.2307-1649
Mofid, Bahram, Mosalla, Sayed Mohammad Modarres, Goodarzi, Masumeh, Tavakoli, Hassan. (1404). 'Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy', سامانه مدیریت نشریات علمی, 15(6), pp. 575-588. doi: 10.31661/jbpe.v0i0.2307-1649
Mofid, Bahram, Mosalla, Sayed Mohammad Modarres, Goodarzi, Masumeh, Tavakoli, Hassan. Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy. سامانه مدیریت نشریات علمی, 1404; 15(6): 575-588. doi: 10.31661/jbpe.v0i0.2307-1649

Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy

Journal of Biomedical Physics and Engineering
دوره 15، شماره 6 - شماره پیاپی 1، اسفند 2025، صفحه 575-588    اصل مقاله (1.46 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2307-1649
نویسندگان
Bahram Mofid1؛ Sayed Mohammad Modarres Mosalla2؛ Masumeh Goodarzi3؛ Hassan Tavakoli* 3، 4
1Department of Radiation Oncology, Shohadae-Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2Department of Nuclear Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran
3Radiation Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
4Department of Physiology and Medical Physics, Baqiyatallah University of Medical Sciences, Tehran, Iran
چکیده
Background: Manual delineation of volumes for prostate radiotherapy treatment is a time-consuming task for radiation oncologists and is also prone to variability. Deep learning-based auto-segmentation methods showed promising results with accurate and high-fidelity contours.
Objective: The objective of this study was to evaluate the feasibility of a Computed Tomography (CT)-based deep learning auto-segmentation algorithm for multi-organ delineation in prostate radiotherapy.
Material and Methods: In this single-institution retrospective study, a total of 118 patients with prostate cancer were included. We applied 3D nnU-net deep convolutional neural network architecture, a self-adapting ensemble method for simultaneous fast and reproducible multi-organ auto-contouring. The dataset was randomly divided into training and test sets from 95 and 23 patients, respectively. Intensity-modulated radiotherapy plans were generated for both manual and automatic delineations using identical optimization settings. Contours were assessed in terms of the Dice Similarity Coefficient (DSC), and average Hausdorff Distance (HD). Dose distributions were additionally evaluated using parameters derived from Dose-Volume Histograms (DVH).
Results: On the test set, 3D nnU-net achieved the best performance in the bladder (DSC:0.97, HD:4.13), right femur head (DSC:0.96, HD:3.58), left femur head (DSC:0.96, HD:3.95), rectum (DSC:0.9, HD:10.04), prostate (DSC:0.82, HD:3.68), lymph nodes (DSC:0.77, HD:15.5), and seminal vesicles (DSC:0.69, HD:10.95). DVH parameters of targets and Organ at Risks (OARs) were significantly different except for lymph nodes and femoral heads between treatment plans based on manual and automatic contours. 
Conclusion: The 3D nnU-net architecture can be successfully used for multi-organ segmentation in the male pelvic area.

تازه های تحقیق

Bahram Mofid (Google Scholar)

Hassan Tavakoli (Google Scholar)

کلیدواژه‌ها
Machine Learning؛ Deep Learning؛ 3D nnU-net؛ Neural Networks؛ Automatic Segmentation؛ Radiotherapy؛ Prostate Cancer
مراجع
  1. Wang L, Lu B, He M, Wang Y, Wang Z, Du L. Prostate Cancer Incidence and Mortality: Global Status and Temporal Trends in 89 Countries From 2000 to 2019. Front Public Health. 2022;10:811044. doi: 10.3389/fpubh.2022.811044. PubMed PMID: 35252092. PubMed PMCID: PMC8888523.
  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49. doi: 10.3322/caac.21660. PubMed PMID: 33538338.
  3. Gay HA, Michalski JM. Radiation Therapy for Prostate Cancer. Mo Med. 2018;115(2):146-50. PubMed PMID: 30228707. PubMed PMCID: PMC6139853.
  4. Baskar R, Lee KA, Yeo R, Yeoh KW. Cancer and radiation therapy: current advances and future directions. Int J Med Sci. 2012;9(3):193-9. doi: 10.7150/ijms.3635. PubMed PMID: 22408567. PubMed PMCID: PMC3298009.
  5. Kosmin M, Ledsam J, Romera-Paredes B, Mendes R, Moinuddin S, De Souza D, et al. Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiother Oncol. 2019;135:130-40. doi: 10.1016/j.radonc.2019.03.004. PubMed PMID: 31015159.
  6. Deeley MA, Chen A, Datteri R, Noble JH, Cmelak AJ, Donnelly EF, et al. Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study. Phys Med Biol. 2011;56(14):4557-77. doi: 10.1088/0031-9155/56/14/021. PubMed PMID: 21725140. PubMed PMCID: PMC3153124.
  7. Chen MY, Woodruff MA, Dasgupta P, Rukin NJ. Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists. Cancer Med. 2020;9(19):7172-82. doi: 10.1002/cam4.3386. PubMed PMID: 32810385. PubMed PMCID: PMC7541146.
  8. Valicenti RK, Sweet JW, Hauck WW, Hudes RS, Lee T, Dicker AP, et al. Variation of clinical target volume definition in three-dimensional conformal radiation therapy for prostate cancer. Int J Radiat Oncol Biol Phys. 1999;44(4):931-5. doi: 10.1016/s0360-3016(99)00090-5. PubMed PMID: 10386652.
  9. Fiorino C, Reni M, Bolognesi A, Cattaneo GM, Calandrino R. Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. Radiother Oncol. 1998;47(3):285-92. doi: 10.1016/s0167-8140(98)00021-8. PubMed PMID: 9681892.
  10. West CM, Huddart RA. Biomarkers and Imaging for Precision Radiotherapy. Clin Oncol (R Coll Radiol). 2015;27(10):545-6. doi: 10.1016/j.clon.2015.06.021. PubMed PMID: 26173954.
  11. Chen AM, Chin R, Beron P, Yoshizaki T, Mikaeilian AG, Cao M. Inadequate target volume delineation and local-regional recurrence after intensity-modulated radiotherapy for human papillomavirus-positive oropharynx cancer. Radiother Oncol. 2017;123(3):412-8. doi: 10.1016/j.radonc.2017.04.015. PubMed PMID: 28511960.
  12. Njeh CF. Tumor delineation: The weakest link in the search for accuracy in radiotherapy. J Med Phys. 2008;33(4):136-40. doi: 10.4103/0971-6203.44472. PubMed PMID: 19893706. PubMed PMCID: PMC2772050.
  13. Kawula M, Purice D, Li M, Vivar G, Ahmadi SA, Parodi K, et al. Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer. Radiat Oncol. 2022;17(1):21. doi: 10.1186/s13014-022-01985-9. PubMed PMID: 35101068. PubMed PMCID: PMC8805311.
  14. Elguindi S, Zelefsky MJ, Jiang J, Veeraraghavan H, Deasy JO, Hunt MA, Tyagi N. Deep learning-based auto-segmentation of targets and organs-at-risk for magnetic resonance imaging only planning of prostate radiotherapy. Phys Imaging Radiat Oncol. 2019;12:80-6. doi: 10.1016/j.phro.2019.11.006. PubMed PMID: 32355894. PubMed PMCID: PMC7192345.
  15. Duan J, Bernard M, Downes L, Willows B, Feng X, Mourad WF, St Clair W, Chen Q. Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process. Med Phys. 2022;49(4):2570-81. doi: 10.1002/mp.15525. PubMed PMID: 35147216.
  16. Sharp G, Fritscher KD, Pekar V, Peroni M, Shusharina N, Veeraraghavan H, Yang J. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014;41(5):050902. doi: 10.1118/1.4871620. PubMed PMID: 24784366. PubMed PMCID: PMC4000389.
  17. Heilemann G, Buschmann M, Lechner W, Dick V, Eckert F, Heilmann M, et al. Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy. Phys Imaging Radiat Oncol. 2023;28:100515. doi: 10.1016/j.phro.2023.100515. PubMed PMID: 38111502. PubMed PMCID: PMC10726238.
  18. Savjani RR, Lauria M, Bose S, Deng J, Yuan Y, Andrearczyk V. Automated Tumor Segmentation in Radiotherapy. Semin Radiat Oncol. 2022;32(4):319-29. doi: 10.1016/j.semradonc.2022.06.002. PubMed PMID: 36202435.
  19. Lustberg T, Van Soest J, Gooding M, Peressutti D, Aljabar P, Van Der Stoep J, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018;126(2):312-7. doi: 10.1016/j.radonc.2017.11.012. PubMed PMID: 29208513.
  20. Cardenas CE, Yang J, Anderson BM, Court LE, Brock KB. Advances in Auto-Segmentation. Semin Radiat Oncol. 2019;29(3):185-97. doi: 10.1016/j.semradonc.2019.02.001. PubMed PMID: 31027636.
  21. Ghaffari H, Tavakoli H, Pirzad Jahromi G. Deep transfer learning-based fully automated detection and classification of Alzheimer’s disease on brain MRI. Br J Radiol. 2022;95(1136):20211253. doi: 10.1259/bjr.20211253. PubMed PMID: 35616643. PubMed PMCID: PMC10162060.
  22. Mohammadi R, Salehi M, Ghaffari H, Reiazi R. Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images. J Biomed Phys Eng. 2020;10(5):559-68. doi: 10.31661/jbpe.v0i0.2008-1153. PubMed PMID: 33134214. PubMed PMCID: PMC7557468.
  23. Salehi M, Ardekani MA, Taramsari AB, Ghaffari H, Haghparast M. Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images. Pol J Radiol. 2022;87:e478-86. doi: 10.5114/pjr.2022.119027. PubMed PMID: 36091652. PubMed PMCID: PMC9453472.
  24. Salehi M, Mohammadi R, Ghaffari H, Sadighi N, Reiazi R. Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images. Br J Radiol. 2021;94(1121):20201263. doi: 10.1259/bjr.20201263. PubMed PMID: 33861150. PubMed PMCID: PMC8506182.
  25. Showkatian E, Salehi M, Ghaffari H, Reiazi R, Sadighi N. Deep learning-based automatic detection of tuberculosis disease in chest X-ray images. Pol J Radiol. 2022;87:e118-24. doi: 10.5114/pjr.2022.113435. PubMed PMID: 35280947. PubMed PMCID: PMC8906182.
  26. Xiao H, Ren G, Cai J. A review on 3D deformable image registration and its application in dose warping. Radiat Med Protect. 2020;1(4):171-8. doi: 10.1016/j.radmp.2020.11.002.
  27. Van Dijk LV, Van Den Bosch L, Aljabar P, Peressutti D, Both S, et al. Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring. Radiother Oncol. 2020;142:15-123. doi: 10.1016/j.radonc.2019.09.022. PubMed PMID: 31653573.
  28. Guo Y, Gao Y, Shen D. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching. IEEE Trans Med Imaging. 2016;35(4):1077-89. doi: 10.1109/TMI.2015.2508280. PubMed PMID: 26685226. PubMed PMCID: PMC5002995.
  29. Liang S, Tang F, Huang X, Yang K, Zhong T, Hu R, et al. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning. Eur Radiol. 2019;29(4):1961-7. doi: 10.1007/s00330-018-5748-9. PubMed PMID: 30302589.
  30. Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15:29. doi: 10.1186/s12880-015-0068-x. PubMed PMID: 26263899. PubMed PMCID: PMC4533825.
  31. Guo H, Wang J, Xia X, Zhong Y, Peng J, Zhang Z, Hu W. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer. Radiat Oncol. 2021;16(1):113. doi: 10.1186/s13014-021-01837-y. PubMed PMID: 34162410. PubMed PMCID: PMC8220801.
  32. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-11. doi: 10.1038/s41592-020-01008-z. PubMed PMID: 33288961.
  33. Anderson BM, Wahid KA, Brock KK. Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays. Pract Radiat Oncol. 2021;11(3):226-9. doi: 10.1016/j.prro.2021.02.003. PubMed PMID: 33607331. PubMed PMCID: PMC8102371.
  34. Balagopal A, Kazemifar S, Nguyen D, Lin MH, Hannan R, Owrangi A, Jiang S. Fully automated organ segmentation in male pelvic CT images. Phys Med Biol. 2018;63(24):245015. doi: 10.1088/1361-6560/aaf11c. PubMed PMID: 30523973.
  35. Tong N, Gou S, Chen S, Yao Y, Yang S, Cao M, et al. Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images. Phys Med Biol. 2021;66(3):035001. doi: 10.1088/1361-6560/abcad9. PubMed PMID: 33197901.
  36. Sultana S, Robinson A, Song DY, Lee J. Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network. J Med Imaging (Bellingham). 2020;7(5):055001. doi: 10.1117/1.JMI.7.5.055001. PubMed PMID: 33102622. PubMed PMCID: PMC7554423.
  37. Wang S, Liu M, Lian J, Shen D. Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy. IEEE Trans Med Imaging. 2021;40(1):310-320. doi: 10.1109/TMI.2020.3025517. PubMed PMID: 32956051. PubMed PMCID: PMC8202780.
  38. Kiljunen T, Akram S, Niemelä J, Löyttyniemi E, Seppälä J, Heikkilä J, et al. A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics (Basel). 2020;10(11):959. doi: 10.3390/diagnostics10110959. PubMed PMID: 33212793. PubMed PMCID: PMC7697786.
  39. Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017;44(12):6377-89. doi: 10.1002/mp.12602. PubMed PMID: 28963779.
  40. Liu Z, Liu X, Xiao B, Wang S, Miao Z, Sun Y, Zhang F. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Phys Med. 2020;69:184-91. doi: 10.1016/j.ejmp.2019.12.008. PubMed PMID: 31918371.
  41. Hwee J, Louie AV, Gaede S, Bauman G, D’Souza D, Sexton T, Lock M, Ahmad B, Rodrigues G. Technology assessment of automated atlas based segmentation in prostate bed contouring. Radiat Oncol. 2011;6:110. doi: 10.1186/1748-717X-6-110. PubMed PMID: 21906279. PubMed PMCID: PMC3180272.
  42. Li D, Zang P, Chai X, Cui Y, Li R, Xing L. Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models. Med Phys. 2016;43(10):5426. doi: 10.1118/1.4962468. PubMed PMID: 27782723. PubMed PMCID: PMC5035314.
  43. Sartor H, Minarik D, Enqvist O, Ulén J, Wittrup A, Bjurberg M, Trägårdh E. Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth. Clin Transl Radiat Oncol. 2020;25:37-45. doi: 10.1016/j.ctro.2020.09.004. PubMed PMID: 33005756. PubMed PMCID: PMC7519211.
  44. Mihelic SA, Sikora WA, Hassan AM, Williamson MR, Jones TA, Dunn AK. Segmentation-Less, Automated, Vascular Vectorization. PLoS Comput Biol. 2021;17(10):e1009451. doi: 10.1371/journal.pcbi.1009451. PubMed PMID: 34624013. PubMed PMCID: PMC8528315.
  45. Zhu Y, Chen L, Lu W, Gong Y, Wang X. The application of the nnU-Net-based automatic segmentation model in assisting carotid artery stenosis and carotid atherosclerotic plaque evaluation. Front Physiol. 2022;13:1057800. doi: 10.3389/fphys.2022.1057800. PubMed PMID: 36561211. PubMed PMCID: PMC9763590.
  46. Huo L, Hu X, Xiao Q, Gu Y, Chu X, Jiang L. Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images. Magn Reson Imaging. 2021;82:31-41. doi: 10.1016/j.mri.2021.06.017. PubMed PMID: 34147598.
آمار
تعداد مشاهده مقاله: 284,484
تعداد دریافت فایل اصل مقاله: 1,003,002


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب