Araştırma Makalesi
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A New Targeting System for Calculating a Trajectory Risk Map According to the Cerebral Vascular Tree

Yıl 2024, Cilt: 5 Sayı: 2, 1 - 13, 31.12.2024

Öz

Determining the precise type of tumor is crucial for selecting the correct treatment method for brain tumors. This study presents a volumetric local risk analysis model that considers spatial risk factors within the volume of the brain biopsy operation region. This risk model can be used to assess the overall risk of the operation region. To estimate the general contribution of risk factors, a piecewise Gaussian probability function is proposed. The superiority of the proposed approach over existing ones lies in its use of a cylindrical structure centered on the pin trajectory to create an entry risk map for all potential entry points on the brain surface. The risk value of pin trajectories is calculated by evaluating the distance of vessel voxels within the cylinder. Using this model, after assessing the risk from surface points to the target, the brain surface is color-coded according to risk values. In practice, by calculating the volumetric local risks of surface points using the ITKTUBETK dataset, a risk map is obtained to predict biopsy points with the lowest risk of vascular damage. For validation purposes, data from 15 patients, marked by two neurosurgeons, were used. Trajectory risks are presented in tables. According to the obtained risk analysis results, the proposed method can identify significantly lower-risk paths, 46.66% and 53.33% more effectively compared to Surgeon1 and Surgeon2, respectively. In future work, integration of fMRI information into the risk model is planned.

Proje Numarası

122E495

Kaynakça

  • Mitchell, V.W. (1995) Organizational Risk Perception and Reduction: A Literature Review. British Journal of Management, 6, 115-133.
  • Macdonald, D. (2004) Practical Hazops, Trips and Alarms. Elsevier, Oxford. Smith, P. G., & Merritt, G. M. (2020). Proactive risk management: Controlling uncertainty in product development. CRC Press.
  • Caplan, J. M., Kennedy, L. W., Barnum, J. D., & Piza, E. L. (2015). Risk terrain modeling for spatial risk assessment. Cityscape, 17(1), 7-16.
  • Queiroz, N., Humphries, N. E., Couto, A., Vedor, M., Da Costa, I., Sequeira, A. M., ... & Sousa, L. L. (2019). Global spatial risk assessment of sharks under the footprint of fisheries. Nature, 572(7770), 461-466.
  • Bai, F., Chisholm, R., Sang, W., & Dong, M. (2013). Spatial risk assessment of alien invasive plants in China. Environmental Science & Technology, 47(14), 7624-7632.
  • Li, F., Huang, J., Zeng, G., Yuan, X., Li, X., Liang, J., ... & Bai, B. (2013). Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China. Journal of Geochemical Exploration, 132, 75-83.
  • Carlon, C., Pizzol, L., Critto, A., & Marcomini, A. (2008). A spatial risk assessment methodology to support the remediation of contaminated land. Environment International, 34(3), 397-411.
  • Li, S., Li, Z., Dong, Y., Shi, T., Zhou, S., Chen, Y., ... & Qin, F. (2023, May). Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters: The case of Shenyang city. In Building simulation (Vol. 16, No. 5, pp. 683-699). Beijing: Tsinghua University Press.
  • Winters, A. M., Eisen, R. J., Delorey, M. J., Fischer, M., Nasci, R. S., Zielinski-Gutierrez, E., ... & Eisen, L. (2010). Spatial risk assessments based on vector-borne disease epidemiologic data: importance of scale for West Nile virus disease in Colorado. The American journal of tropical medicine and hygiene, 82(5), 945.
  • Gaffari Çelik. Muhammed Fatih Talu , A new 3D MRI segmentation method based on generative adversarial network and atrous convolution Biomed. Signal Process. Control (2022) Volume 71, Part A, 2022, 103155, ISSN 1746-8094. Lynagh R, Ishak M, Georges J, Lopez D, Osman H, Kakareka M, et al. Fluorescence-guided stereotactic biopsy: a proof-of-concept study. J Neurosurg. (2019) 22:1–7. doi: 10.3171/2018.11.JNS18629.
  • Zanello, M., Carron, R., Peeters, S. et al..Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives.Neurosurg Rev 44, 867-888 (2021). https://doi.org/10.1007/s10143-020-01315-1. Hu Yue, Cai Pu, Zhang Huawei, Adilijiang Aihemaitiniyazi, Peng Jun, Li Yun, Che Shanli, Lan Fei, Liu Changqing, (2022), A Comparation Between Frame-Based and Robot-Assisted in Stereotactic Biopsy, Frontiers in Neurology, Vol=13, Issn=1664-2295, https://doi.org/10.3389/fneur.2022.928070.
  • Jung, I-H, Chang, KW, Park, SH, et al. Stereotactic biopsy for adult brainstem lesions: A surgical approach and its diagnostic value according to the 2016 World Health Organization Classification. Cancer Med. 2021; 10: 7514– 7524. https://doi.org/10.1002/cam4.4272.
  • Trope, M., Shamir, R.R., Joskowicz, L. et al. The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery. Int J CARS10, 1127–1140 (2015). https://doi.org/10.1007/s11548-014-1126-5.
  • ITKTubeTK dataset shared on https://public.kitware.com/Wiki/TubeTK [Online] Erişim Tarihi: 16.04.2022. Nagwa, “Lesson explainer: The Perpendicular distance between points and straight lines in Space Mathematics,” Nagwa. [Online].
  • Kikinis R, Pieper SD, Vosburgh K (2014) 3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support. Intraoperative Imaging Image-Guided Therapy, Ferenc A. Jolesz, Editor 3(19):277–289 ISBN: 978-1-4614-7656-6 (Print) 978-1-4614-7657-3 (Online).
  • Kapur, Tina; Pieper, Steve; Fedorov, Andriy; Fillion-Robin, J-C; Halle, Michael; O'Donnell, Lauren; Lasso, Andras; Ungi, Tamas; Pinter, Csaba; Finet, Julien; Pujol, Sonia; Jagadeesan, Jayender; Tokuda, Junichi; Norton, Isaiah; Estepar, Raul San Jose; Gering, David; Aerts, Hugo J W L; Jakab, Marianna; Hata, Nobuhiko; Ibanez, Luiz; Blezek, Daniel; Miller, Jim; Aylward, Stephen; Grimson, W Eric L; Fichtinger, Gabor; Wells, William M; Lorensen, William E; Schroeder, Will; Kikinis, Ron; 2016. “Increasing the Impact of Medical Image Computing Using Community-Based Open-Access Hackathons: The NA-MIC and 3D Slicer Experience.” Medical Image Analysis 33 (October): 176–80.
  • Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012 Nov;30(9):1323-41. PMID: 22770690. PMCID: PMC3466397.
  • Pieper S, Lorensen B, Schroeder W, Kikinis R. The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as an Open Platform for the Medical Image Computing Community. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2006; 1:698-701.
  • Pieper S, Halle M, Kikinis R. 3D SLICER. Proceedings of the 1st IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2004; 1:632-635.
  • Gering D.T., Nabavi A., Kikinis R., Hata N., O'Donnell L., Grimson W.E.L., Jolesz F.A., Black P.M., Wells III W.M. An Integrated Visualization System for Surgical Planning and Guidance using Image Fusion and an Open MR. J Magn Reson Imaging. 2001 Jun;13(6):967-75. PMID: 11382961.
  • Gering D.T., Nabavi A., Kikinis R., Grimson W.E.L., Hata N., Everett P., Jolesz F.A., Wells III W.M. An Integrated Visualization System for Surgical Planning and Guidance using Image Fusion and Interventional Imaging. Int Conf Med Image Comput Comput Assist Interv. 1999 Sep;2:809-19.
  • 3D Slicer application available at: https://www.slicer.org/ [Online] Date of Access: 09.04.2022.
  • Isensee F, Schell M, Tursunova I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer HP, Heiland S, Wick W, Bendszus M, Maier-Hein KH, Kickingereder P. Automated brain extraction of multi-sequence MRI using artificial neural networks. Hum Brain Mapp. 2019; 1–13.

Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi

Yıl 2024, Cilt: 5 Sayı: 2, 1 - 13, 31.12.2024

Öz

Beyin tümörlerinde doğru tedavi yönteminin belirlenebilmesinde tümör tipinin kesin olarak bilinmesi kritiktir. Bu çalışma, beyin biyopsisi operasyon bölgesi hacmi içindeki mekansal risk faktörlerini gözeten hacimsel yerel risk analizi modeli sunmaktadır. Bu risk modeli, operasyon bölgesinin genel riskini değerlendirmek için kullanılabilir. Risk faktörlerinin genel katkısını tahmin etmek amacıyla, parça bazlı Gauss olasılık fonksiyonu önerilmektedir. Önerilen yaklaşımın mevcut benzerlerinden üstünlüğü, beyin yüzeyindeki tüm aday giriş noktalarının giriş risk haritasını oluştururken, pin-yörüngesinin merkezinde bulunan silindirik bir yapı kullanmasıdır. Pin-yörüngelerinin risk değeri, silindir içindeki damar voksellerini mesafesi değerlendirilerek hesaplanır. Bu modeli kullanarak beyin yüzeyindeki noktalardan hedefe olan risk değerlendirmesinin ardından beyin yüzeyi risk değerlerine göre renklendirilmektedir. Uygulamada, beyin yüzeyindeki noktaların hacimsel yerel riskleri hesaplanarak damar hasarlarının en düşük riskine sahip biyopsi noktalarını tahmin etmek için bir risk haritası elde edilmiştir. Doğrulama amacıyla, 2 beyin cerrahı tarafından işaretlenmiş 15 hasta verisi kullanılmıştır. Yörünge riskleri tablolarda sunulmuştur. Sonraki çalışmada, fMRI bilgilerinin risk modeline entegrasyonu planlanmaktadır.

Etik Beyan

Bu makale ilk defa burada paylaşılmaktadır.

Destekleyen Kurum

Tübitak

Proje Numarası

122E495

Teşekkür

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 122E495 numaralı proje ile desteklenmiştir. Projeye verdiği destekten ötürü TÜBİTAK’a teşekkürlerimizi sunarız.

Kaynakça

  • Mitchell, V.W. (1995) Organizational Risk Perception and Reduction: A Literature Review. British Journal of Management, 6, 115-133.
  • Macdonald, D. (2004) Practical Hazops, Trips and Alarms. Elsevier, Oxford. Smith, P. G., & Merritt, G. M. (2020). Proactive risk management: Controlling uncertainty in product development. CRC Press.
  • Caplan, J. M., Kennedy, L. W., Barnum, J. D., & Piza, E. L. (2015). Risk terrain modeling for spatial risk assessment. Cityscape, 17(1), 7-16.
  • Queiroz, N., Humphries, N. E., Couto, A., Vedor, M., Da Costa, I., Sequeira, A. M., ... & Sousa, L. L. (2019). Global spatial risk assessment of sharks under the footprint of fisheries. Nature, 572(7770), 461-466.
  • Bai, F., Chisholm, R., Sang, W., & Dong, M. (2013). Spatial risk assessment of alien invasive plants in China. Environmental Science & Technology, 47(14), 7624-7632.
  • Li, F., Huang, J., Zeng, G., Yuan, X., Li, X., Liang, J., ... & Bai, B. (2013). Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China. Journal of Geochemical Exploration, 132, 75-83.
  • Carlon, C., Pizzol, L., Critto, A., & Marcomini, A. (2008). A spatial risk assessment methodology to support the remediation of contaminated land. Environment International, 34(3), 397-411.
  • Li, S., Li, Z., Dong, Y., Shi, T., Zhou, S., Chen, Y., ... & Qin, F. (2023, May). Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters: The case of Shenyang city. In Building simulation (Vol. 16, No. 5, pp. 683-699). Beijing: Tsinghua University Press.
  • Winters, A. M., Eisen, R. J., Delorey, M. J., Fischer, M., Nasci, R. S., Zielinski-Gutierrez, E., ... & Eisen, L. (2010). Spatial risk assessments based on vector-borne disease epidemiologic data: importance of scale for West Nile virus disease in Colorado. The American journal of tropical medicine and hygiene, 82(5), 945.
  • Gaffari Çelik. Muhammed Fatih Talu , A new 3D MRI segmentation method based on generative adversarial network and atrous convolution Biomed. Signal Process. Control (2022) Volume 71, Part A, 2022, 103155, ISSN 1746-8094. Lynagh R, Ishak M, Georges J, Lopez D, Osman H, Kakareka M, et al. Fluorescence-guided stereotactic biopsy: a proof-of-concept study. J Neurosurg. (2019) 22:1–7. doi: 10.3171/2018.11.JNS18629.
  • Zanello, M., Carron, R., Peeters, S. et al..Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives.Neurosurg Rev 44, 867-888 (2021). https://doi.org/10.1007/s10143-020-01315-1. Hu Yue, Cai Pu, Zhang Huawei, Adilijiang Aihemaitiniyazi, Peng Jun, Li Yun, Che Shanli, Lan Fei, Liu Changqing, (2022), A Comparation Between Frame-Based and Robot-Assisted in Stereotactic Biopsy, Frontiers in Neurology, Vol=13, Issn=1664-2295, https://doi.org/10.3389/fneur.2022.928070.
  • Jung, I-H, Chang, KW, Park, SH, et al. Stereotactic biopsy for adult brainstem lesions: A surgical approach and its diagnostic value according to the 2016 World Health Organization Classification. Cancer Med. 2021; 10: 7514– 7524. https://doi.org/10.1002/cam4.4272.
  • Trope, M., Shamir, R.R., Joskowicz, L. et al. The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery. Int J CARS10, 1127–1140 (2015). https://doi.org/10.1007/s11548-014-1126-5.
  • ITKTubeTK dataset shared on https://public.kitware.com/Wiki/TubeTK [Online] Erişim Tarihi: 16.04.2022. Nagwa, “Lesson explainer: The Perpendicular distance between points and straight lines in Space Mathematics,” Nagwa. [Online].
  • Kikinis R, Pieper SD, Vosburgh K (2014) 3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support. Intraoperative Imaging Image-Guided Therapy, Ferenc A. Jolesz, Editor 3(19):277–289 ISBN: 978-1-4614-7656-6 (Print) 978-1-4614-7657-3 (Online).
  • Kapur, Tina; Pieper, Steve; Fedorov, Andriy; Fillion-Robin, J-C; Halle, Michael; O'Donnell, Lauren; Lasso, Andras; Ungi, Tamas; Pinter, Csaba; Finet, Julien; Pujol, Sonia; Jagadeesan, Jayender; Tokuda, Junichi; Norton, Isaiah; Estepar, Raul San Jose; Gering, David; Aerts, Hugo J W L; Jakab, Marianna; Hata, Nobuhiko; Ibanez, Luiz; Blezek, Daniel; Miller, Jim; Aylward, Stephen; Grimson, W Eric L; Fichtinger, Gabor; Wells, William M; Lorensen, William E; Schroeder, Will; Kikinis, Ron; 2016. “Increasing the Impact of Medical Image Computing Using Community-Based Open-Access Hackathons: The NA-MIC and 3D Slicer Experience.” Medical Image Analysis 33 (October): 176–80.
  • Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012 Nov;30(9):1323-41. PMID: 22770690. PMCID: PMC3466397.
  • Pieper S, Lorensen B, Schroeder W, Kikinis R. The NA-MIC Kit: ITK, VTK, Pipelines, Grids and 3D Slicer as an Open Platform for the Medical Image Computing Community. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2006; 1:698-701.
  • Pieper S, Halle M, Kikinis R. 3D SLICER. Proceedings of the 1st IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2004; 1:632-635.
  • Gering D.T., Nabavi A., Kikinis R., Hata N., O'Donnell L., Grimson W.E.L., Jolesz F.A., Black P.M., Wells III W.M. An Integrated Visualization System for Surgical Planning and Guidance using Image Fusion and an Open MR. J Magn Reson Imaging. 2001 Jun;13(6):967-75. PMID: 11382961.
  • Gering D.T., Nabavi A., Kikinis R., Grimson W.E.L., Hata N., Everett P., Jolesz F.A., Wells III W.M. An Integrated Visualization System for Surgical Planning and Guidance using Image Fusion and Interventional Imaging. Int Conf Med Image Comput Comput Assist Interv. 1999 Sep;2:809-19.
  • 3D Slicer application available at: https://www.slicer.org/ [Online] Date of Access: 09.04.2022.
  • Isensee F, Schell M, Tursunova I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer HP, Heiland S, Wick W, Bendszus M, Maier-Hein KH, Kickingereder P. Automated brain extraction of multi-sequence MRI using artificial neural networks. Hum Brain Mapp. 2019; 1–13.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Mustafa Şahin 0009-0006-1701-4566

Muhammed Fatih Talu 0000-0003-1166-8404

Proje Numarası 122E495
Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 3 Haziran 2024
Kabul Tarihi 11 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 5 Sayı: 2

Kaynak Göster

APA Şahin, M., & Talu, M. F. (2024). Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 5(2), 1-13.
AMA Şahin M, Talu MF. Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. Aralık 2024;5(2):1-13.
Chicago Şahin, Mustafa, ve Muhammed Fatih Talu. “Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 5, sy. 2 (Aralık 2024): 1-13.
EndNote Şahin M, Talu MF (01 Aralık 2024) Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 5 2 1–13.
IEEE M. Şahin ve M. F. Talu, “Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi”, Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 5, sy. 2, ss. 1–13, 2024.
ISNAD Şahin, Mustafa - Talu, Muhammed Fatih. “Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 5/2 (Aralık 2024), 1-13.
JAMA Şahin M, Talu MF. Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. 2024;5:1–13.
MLA Şahin, Mustafa ve Muhammed Fatih Talu. “Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi”. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 5, sy. 2, 2024, ss. 1-13.
Vancouver Şahin M, Talu MF. Beyin Damar Ağacına Göre Trajeksiyon Risk Haritasını Hesaplayan Yeni Bir Hedefleme Sistemi. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi. 2024;5(2):1-13.