Mask R-CNN kullanılarak yeni bir MRG veri tabanında prostat bölgelerinin segmentasyonu: PACS sistemi üzerinde bir uygulama

Year 2024, Volume: 39 Issue: 3, 1401 - 1416

Çağlar Gürkan Abdulkadir Budak Hakan Karataş Kayıhan Akın

https://doi.org/10.17341/gazimmfd.1153507

Abstract

Akciğer kanserinden sonra erkeklerde en yaygın rastlanan kanser türü prostat kanseridir. Günümüzde, ileri prostat görüntüleme radyologlar tarafından yapılan multiparametrik prostat manyetik rezonans görüntüleme (MRG) ile gerçekleştirilmektedir. Prostatın birçok patolojisi görüntülenebilse de, asıl amaç prostat kanseri olasılığını belirlemek ve biyopsi işlemine gerek olup olmadığına karar vermektir. Bu sürece, T2 ağırlıklı görüntüler (T2W), difüzyon ağırlıklı görüntüler (DWI) ve dinamik kontrastlı görüntüler (DCE) olmak üzere farklı seriler halindeki MRG görüntülerinin analizi dahil edilmektedir. Bununla birlikte, öncelikle prostat bölgelerinin ayrıştırılması gerekmektedir. Daha sonra ilgili prostat bölgelerinde lezyon taraması yapılmaktadır. Son olarak ise prostat lezyon skorlama işleminin PI-RADS v2’ye göre yapılmasına ihtiyaç duyulmaktadır. Bu nedenle prostat kanseri tanısının konulması karışık ve uzun bir süreçtir. Bu sebeble, prostat kanseri tanısının koyulması için karar destek sistemlerine ihtiyaç duyulmaktadır. Bu bağlamda, çalışmanın başlıca amacı prostat bölgelerinin otomatik olarak segmentasyonunu sağlamaktır. Segmentasyon görevinde 15 hastaya ait T2W MRG görüntüleri ile birlikte Mask R-CNN algoritması kullanılmıştır. Mask R-CNN algoritması ResNet-50 omurga modelinin kullanımı ile birlikte 96,040 mAP50 değeri ile segmentasyon performansı elde etmiştir. Son olarak, eğitilen model PACS sistemine entegre edilmiştir. Entegrasyon sayesinde hastanelerde kullanıma hazır bir yapay zeka destekli karar destek sistemi geliştirilmiştir. Böylelikle, sağlık çalışanları üzerindeki iş yükü azaltılırken zamandan da kazanç sağlanmıştır.

Keywords

Prostat bölge segmentasyonu, prostat kanseri, T2W MRG, PI-RADS v2, derin öğrenme

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Project Number

3191419

Thanks

Bu proje, 3191419 proje numarası ile Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmektedir. Bu projenin¸ yürütülmesinde her türlü olanağı sağlayan Akgün Bilgisayar’a teşekkür ederiz.

Segmentation of prostate zones on a novel MRI database using Mask R-CNN: an implementation on PACS system

Abstract

After lung cancer, prostate cancer is the most common type of cancer in men. Nowadays, advanced prostate imaging is conducted by multiparametric prostate magnetic resonance imaging (MRI) performed by radiologists. Although many pathologies of the prostate can be visualized, the main purpose is to determine the probability of prostate cancer and to decide whether a biopsy is needed. This process includes the analysis of different series of Magnetic Resonance (MR) images, including T2-weighted images (T2W), diffuse-weighted images (DWI), and dynamic contrast enhanced images (DCE). However, firstly it is necessary to differentiate the prostate zones. Then, lesion scanning is performed in the relevant prostate zones. Finally, there is a need to perform prostate lesion scoring according to PI-RADS v2. Therefore, diagnosis of prostate cancer is a complex and long process. For this reason, decision support systems are needed for the diagnosis of prostate cancer. In this context, the main purpose of the study is to provide automatic segmentation of prostate regions. In the segmentation task, Mask R-CNN algorithm was used with T2W MR images of 15 patients. With the use of the ResNet-50 backbone model, the Mask R-CNN algorithm achieved the segmentation performance with a mAP50 value of 96.040. Finally, the trained model was integrated into the PACS system. Thanks to the integration, a ready-to-use in hospitals artificial intelligence-supported decision support system was developed. Thus, time was saved while reducing the workload on health employees.

Keywords

Prostate zone segmentation, prostate cancer, T2W MRI, PI-RADS v2, deep learning

Project Number

3191419

References

• 1. Jemal A., Siegel R., Ward E., Hao Y., Xu J., Thun M.J., Cancer statistics, 2009, CA Cancer J. Clin., 59 (4), 225–249, 2009.
• 2. American Cancer Society. Information and resources about for cancer: Breast, colon, lung, prostate. Phytochemicals. https://www.cancer.org/. Yayın tarihi 2000. Erişim tarihi Ağustos 02, 2022.
• 3. Mizuno K., Beltran H., Future directions for precision oncology in prostate cancer, The Prostate, 82(S1), 2022.
• 4. Siegel R.L., Miller K.D., Jemal A. Cancer statistics, 2019. CA Cancer J. Clin., 69 (1), 7–34, 2019.
• 5. Martin R.M., Vatten L., Gunnell D., Romundstad P., Blood pressure and risk of prostate cancer: Cohort Norway (CONOR), Cancer Causes Control, 21 (3), 463–472, 2010.
• 6. Venkateswaran V., Klotz L.H., Diet and prostate cancer: Mechanisms of action and implications for chemoprevention, Nature Reviews Urology, 7 (8), 442–453, 2010.
• 7. Alexander D.D., Mink P.J., Cushing C.A., Sceurman B., A review and meta-analysis of prospective studies of red and processed meat intake and prostate cancer. Nutr. J., 9 (50), 2010. 8. Tarver T., Cancer facts & figures 2012. American Cancer Society (ACS), J. Consum Health, 16 (3), 366–367, 2012.
• 9. Giovannucci E., Liu Y., Platz E.A., Stampfer M.J., Willett W.C., Risk factors for prostate cancer incidence and progression in the health professionals follow-up study, Int. J. Cancer, 121 (7), 1571–1578, 2007.
• 10. Rodriguez C, Freedland S.J., Deka A., Jacobs E.J., McCullough M.L., Patel A.V., Thun M.J., Calle E.E., Body mass index, weight change, and risk of prostate cancer in the Cancer Prevention Study II Nutrition Cohort, Cancer Epidemiol Biomarkers Prev., 16 (1), 63–69, 2007.
• 11. Steinberg G.D., Carter B.S., Beaty T.H., Childs B., Walsh P.C., Family history and the risk of prostate cancer, The Prostate, 17 (4), 337–347, 1990.
• 12. Hoeks C.M.A., Barentsz J.O., Hambrock T., Yakar D., Somford D.M., Heijmink S.W.T.P.J., Scheenen T.W.J, Vos P.C., Huisman H., Van Oort I.M., Witjes J.A., Heerschap A., Fütterer J.J., Prostate cancer: Multiparametric MR imaging for detection, localization, and staging, Radiology, 261 (1), 46–66, 2011.
• 13. Junker D., Schäfer G., Kobel C., Kremser C., Bektic J., Jaschke W., Aigner F., Comparison of real-time elastography and multiparametric MRI for prostate cancer detection: A whole-mount step-section analysis, Am. J. Roentgenol, 202 (3), 2014.
• 14. McNeal J.E., The zonal anatomy of the prostate, The Prostate, 2 (1), 35–49, 1981.
• 15. Weinreb J.C., Barentsz J.O., Choyke P.L., Cornud F., Haider M.A., Macura K.J., Margolis, D., Schnall, M.D., Shtern, F., Tempany, C.M., Thoeny, H.C., Verma, S., PI-RADS prostate imaging - reporting and data system: 2015, version 2. Eur. Urol., 69 (1), 16–40, 2016.
• 16. Droste R., Cai Y., Sharma H., Chatelain P., Drukker L., Papageorghiou A.T., Noble, J.A., Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention, Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, Cilt 11492, Editör: Chung A.C.S., Gee J.C., Yushkevich P.A., Bao S., Springer Cham, Zürih, İsviçre, 592–604, 2019.
• 17. Seçgin A., Kara M., Güler S., Akciğer nodül özelliklerinin tahmininde çeşitli sınıflama stratejilerinin incelenmesi. Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (2), 709–726, 2019.
• 18. Arı A., Hanbay D., Bölgesel evrişimsel sinir ağları tabanlı MR görüntülerinde tümör tespiti, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (3), 1395–1408, 2019.
• 19. Karaci A., X-ışını görüntülerinden omuz implantlarının tespiti ve sınıflandırılması: YOLO ve önceden eğitilmiş evrişimsel sinir ağı tabanlı bir yaklaşım, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (1), 283–294, 2022.
• 20. Gürkahraman K., Karakiş R., Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (2), 997–1012, 2021.
• 21. Yilmaz A. Çok kanallı CNN mimarisi ile X-Ray görüntülerinden COVID-19 tanısı, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (4), 1761–1774, 2021.
• 22. Balakrishnan G., Zhao A., Sabuncu M.R., Guttag J., Dalca A.V., VoxelMorph: A learning framework for deformable medical image registration, IEEE Trans. Med. Imaging, 38 (8), 1788–1800, 2019.
• 23. Isensee F., Petersen J., Klein A., Zimmerer D., Jaeger P.F., Kohl S., Wasserthal J., Koehler G., Norajitra T., Wirkert S., Maier-Hein K.H., nnU-Net: Self-adapting framework for U-Net-based medical image segmentation, 2019.
• 24. Cuocolo R., Comelli A., Stefano A., Benfante V., Dahiya N., Stanzione A., Castaldo A., De Lucia D.R., Yezzi A., Imbriaco M., Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset, J. Magn. Reson. Imaging, 54 (2), 452–459, 2021.
• 25. Duran A., Dussert G., Rouvière O., Jaouen T., Jodoin P.M., Lartizien C., ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans, Med. Image Anal., 77, 2022.
• 26. Mooij G., Bagulho I., Huisman H., Automatic segmentation of prostate zones, 2018.
• 27. Aldoj N., Biavati F., Michallek F., Stober S., Dewey M., Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net. Sci. Rep., 10 (1), 2020.
• 28. van Sloun R.J.G., Wildeboer R.R., Mannaerts C.K., Postema A.W., Gayet M., Beerlage H.P., Salomon G., Wijkstra H., Mischi M., Deep learning for real-time, automatic, and scanner-adapted prostate (zone) segmentation of transrectal ultrasound, for example, magnetic resonance imaging–transrectal ultrasound fusion prostate biopsy, Eur. Urol. Focus, 7 (1), 78–85, 2021.
• 29. Liu Y., Sung K., Yang G., Afshari Mirak S., Hosseiny M., Azadikhah A., Zhong X., Reiter R.E., Lee Y., Raman S.S., Automatic prostate zonal segmentation using fully convolutional network with feature pyramid attention, IEEE Access, 7, 163626–163632, 2019.
• 30. Bardis M., Houshyar R., Chantaduly C., Tran-Harding K., Ushinsky A., Chahine C., Rupasinghe M., Chow D., Chang P., Segmentation of the prostate transition zone and peripheral zone on mr images with deep learning, Radiol Imaging Cancer, 3 (3), 2021.
• 31. Meyer A., Rakr M., Schindele D., Blaschke S., Schostak M., Fedorov A., Hansen C., Towards Patient-Individual Pi-rads v2 Sector Map: CNN for Automatic Segmentation of Prostatic Zones from T2-Weighted MRI, International Symposium on Biomedical Imaging, Venedik-İtalya, 696–700, 8-11 Nisan, 2019.
• 32. Rundo L., Han C., Nagano Y., Zhang J., Hataya R., Militello C., Tangherloni A., Nobile M.S., Ferretti C., Besozzi D., Gilardi M.C., Vitabile S., Mauri G., Nakayama H., Cazzaniga P., USE-Net: Incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets, Neurocomputing, 365, 31–43, 2019.
• 33. Montagne S., Hamzaoui D., Allera A., Ezziane M., Luzurier A., Quint R., Kalai M., Ayache N., Delingette H., Renard-Penna R., Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology, Insights Imaging, 12 (1), 2021.
• 34. Rouvière O., Moldovan P.C., Vlachomitrou A., Gouttard S., Riche B., Groth A., Rabotnikov M., Ruffion A., Colombel M., Crouzet S., Weese J., Rabilloud M., Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation, Eur. Radiol., 32 (5), 3248–3259, 2022.
• 35. Zhu Y., Wei R., Gao G., Ding L., Zhang X., Wang X., Zhang J., Fully automatic segmentation on prostate MR images based on cascaded fully convolution network, J. Magn. Reson. Imaging, 49 (4), 1149–1156, 2019.
• 36. Khan Z., Yahya N., Alsaih K., Ali S.S.A., Meriaudeau F., Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI, Sensors, 20 (11), 1–17, 2020.
• 37. Sunoqrot M.R.S., Selnæs K.M., Sandsmark E., Langørgen S., Bertilsson H., Bathen T.F., Elschot M., The reproducibility of deep learning-based segmentation of the prostate gland and zones on t2-weighted mr images, Diagnostics, 11 (9), 2021.
• 38. Qin X., Zhu Y., Wang W., Gui S., Zheng B., Wang P., 3D multi-scale discriminative network with multi-directional edge loss for prostate zonal segmentation in bi-parametric MR images, Neurocomputing, 418, 148–161, 2020.
• 39. Gurkan C., Kozalioglu S., Palandoken M., Real time mask detection, social distance and crowd analysis using convolutional neural networks and YOLO architecture designs. Acad. Perspect. Procedia, 4 (1), 195–204, 2021.
• 40. Wu Y., Kirillov A., Massa F., Lo W.Y., Girshick R., Detectron2, 2019.
• 41. He K., Zhang X., Ren S., Sun J., Deep Residual Learning for Image Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas-A.B.D., 770–778, 26 Haziran-1 Temmuz, 2016.
• 42. Xie S., Girshick R., Dollár P., Tu Z., He K., Aggregated Residual Transformations for Deep Neural Networks, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu-Hawaii, 5987–5995, 21-26 Temmuz, 2017.