Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış

Furkan Atlan; İhsan Pençe

Derleme

Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış

Yıl 2021, Cilt: 5 Sayı: 1, 207 - 230, 29.06.2021

Furkan Atlan , İhsan Pençe

Öz

Günümüzde yapay zekânın kullanıldığı alanlar her geçen gün artmakta olup, bu alanlardan biri de sağlık sektörüdür. Özellikle görüntü işlemede oldukça başarılı sonuçlar vermesi sebebi ile yapay zekânın bir alt dalı olan derin öğrenme, tıbbi görüntülerin işlenmesinde ve yorumlanmasında sıkça tercih edilmektedir. Her ne kadar tıbbi görüntüleme teknolojilerinin gelişmesi ile hastalık tanısı ve teşhisi gibi işlemlerdeki doğruluk oranı artsa da bu görüntülerin uzmanlar tarafından doğru bir şekilde yorumlanması zaman açısından maliyetli ve tedavi süreci açısından da olumsuz bir durum sergilemektedir. Bu sebeple, yapay zekâ kullanılarak otomatik tanı sistemleri oluşturulmakta ve bu sistemler gelişen teknoloji ve algoritmalar sayesinde her geçen gün ilerleme kat etmektedir. Çalışmanın amacı, tıbbi görüntülemede yapay zekâ kullanımı konusunda tüm bileşenlerin ele alınarak bilgi verilmesi ve bu alanda çalışma yapacak araştırmacılara bir temel teşkil edecek bir alt yapı oluşturmaktır. Bunun sağlanması için yapay zekâ ve tıbbi görüntüleme konusu öncelikle ayrı bir şekilde ele alınmış, tıbbi görüntüleme teknolojileri kapsamlı bir şekilde anlatılmış ve tıbbi görüntülemede yapay zekâ kullanımının mevcut durumu, geleceği, sorunları ve çözümleri açık bir şekilde belirtilmiştir. Son olarak yapay zekâ teknikleri ile tıbbi görüntülerin işlenmesine dair çalışmalar verilerek çalışmanın teorik anlam bütünlüğü sağlanmıştır.

Anahtar Kelimeler

Tıbbi Görüntüleme Teknolojileri, Yapay Zekâ, Bilgisayar Destekli Tanı Sistemleri

Kaynakça

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Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495.
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Budak, Ü. (2019). SegNet Mimarisi ile Bilgisayarlı Tomografi Görüntülerinden Karaciğer Bölgesinin Bölütlenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(1), 215-222.
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Cheng, H. D., Cai, X., Chen, X., Hu, L., & Lou, X. (2003). Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern recognition, 36(12), 2967-2991.
Chow, J. C., Boyd, S. K., Lichti, D. D., & Ronsky, J. L. (2020). Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy. IEEE Transactions on Medical Imaging, 39(6), 2051-2060.
Coşkun, Y. (2019). Ayrık dalgacık dönüşümü tabanlı paralel görüntü sıkıştırma sistemi tasarımı (Master’s thesis, Maltepe Üniversitesi, Fen Bilimleri Enstitüsü). Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
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Dandıl, E., Seri̇n, Z. (2020). Derin Sinir Ağları Kullanarak Histopatolojik Görüntülerde Meme Kanseri Tespiti . Avrupa Bilim ve Teknoloji Dergisi , Ejosat Özel Sayı 2020 (HORA): 451-463.
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An Overview of Artificial Intelligence and Medical Imaging Technologies

Yıl 2021, Cilt: 5 Sayı: 1, 207 - 230, 29.06.2021

Furkan Atlan , İhsan Pençe

Öz

Nowadays, the use of artificial intelligence is increasing steadily, particularly in the health sector. Deep learning, which is a sub-branch of artificial intelligence, is frequently preferred in the processing and interpretation of medical images, because it provides fruitful outcomes in image processing. Despite the development in medical imaging technologies and the increasing accuracy rate of disease diagnosis, accurate interpretation of these images by experts is time consuming, and unfavorable conditions may arise during treatment. For this reason, automated diagnostic systems are created using artificial intelligence, and these systems are improving gradually, owing to the evolution of several technologies and algorithms. This study aimed to provide information on the use of artificial intelligence in medical imaging with due consideration of all factors and create a base infrastructure for researchers in this field. To achieve this, previously artificial intelligence and medical imaging were discussed separately, placing more emphasis on medical imaging technologies. However, at present, potential problems and solutions in the use of artificial intelligence in medical imaging are clearly stated. In conclusion, by conducting more studies on the processing of medical images using artificial intelligence, the theoretical integrity of this field will become possible.

Anahtar Kelimeler

Medical imaging technologies, Artificial intelligence, Computer-aided diagnosis systems

Kaynakça

3 Boyutlu Mamogram Görüntüsü. WAKE RADIOLOGY. (2020, 8 Haziran). Erişim Adresi: https://www.wakerad.com/whats-new/ study-3-d-scans-accurate-standard-mammograms/
ABD’li ve Çinli şirketler Yapay Zekaya hükmetmek için yarışıyor. (2020, 17 Ekim). Erişim Adresi: https://www.wsj.com/articles/ why-u-s-companies-may-losethe-ai-race-1516280677
Alp, H., Akıncı, T. Ç., & Albora, M. (2008). Jeofizik Uygulamalarda Fourier ve Dalgacık Dönüşümlerinin Karşılaştırılması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 14(1), 67-76.
Al-shamasneh, A. R. M., & Obaidellah, U. H. B. (2017). Artificial intelligence techniques for cancer detection and classification: review study. European Scientific Journal, 13(3), 342-370. Arslan, T. X Işınları ve Kullanım Alanları. Gazi Üniversitesi Tezi, 2010.
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495.
Bailey, D. L., Maisey, M. N., Townsend, D. W., & Valk, P. E. (2005). Positron emission tomography (Vol. 2). London: Springer. Bankman, I. N. (2009). Handbook of medical image processing and analysis. Boston: Academic Press.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, vol. 6, no. 8, pp. 394-424.
Buck, A. K., Nekolla, S., Ziegler, S., Beer, A., Krause, B. J., Herrmann, K., ... & Drzezga, A. (2008). Spect/ct. Journal of Nuclear Medicine, 49(8), 1305-1319.
Budak, Ü. (2019). SegNet Mimarisi ile Bilgisayarlı Tomografi Görüntülerinden Karaciğer Bölgesinin Bölütlenmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(1), 215-222.
Bushberg, J. T., Seibert, J. A., Leidholdt Jr, E. M., Boone, J. M., & Goldschmidt Jr, E. J. (2011). Introduction to Medical Imaging. The essential physics of medical imaging. 3 rd edition: Lippincott Williams & Wilkins.
Buxton, R. B. (2009). Introduction to Functional Magnetic Resonance Imaging Principles and Techniques. Cambridge: Cambridge University Press.
Cai, C., Wang, C., Zeng, Y., Cai, S., Liang, D., Wu, Y., Chen, Z., Ding, X. and Zhong, J. (2018). Single‐shot T2 mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network. Magnetic resonance in medicine, 80(5), 2202-2214.
Cheng, H. D., Cai, X., Chen, X., Hu, L., & Lou, X. (2003). Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern recognition, 36(12), 2967-2991.
Chow, J. C., Boyd, S. K., Lichti, D. D., & Ronsky, J. L. (2020). Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy. IEEE Transactions on Medical Imaging, 39(6), 2051-2060.
Coşkun, Y. (2019). Ayrık dalgacık dönüşümü tabanlı paralel görüntü sıkıştırma sistemi tasarımı (Master’s thesis, Maltepe Üniversitesi, Fen Bilimleri Enstitüsü). Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
Çelik, G., & Talu, M. F. (2019). Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 181-192.
Dandıl, E., Seri̇n, Z. (2020). Derin Sinir Ağları Kullanarak Histopatolojik Görüntülerde Meme Kanseri Tespiti . Avrupa Bilim ve Teknoloji Dergisi , Ejosat Özel Sayı 2020 (HORA): 451-463.
Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A., & Fei-Fei, L. (2012). Imagenet large scale visual recognition competition 2012 (ILSVRC2012).
Doi, K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics, 31(4-5), 198-211.
Dong, X., Lei, Y., Wang, T., Higgins, K., Liu, T., Curran, W. J., ... & Yang, X. (2020). Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Physics in Medicine & Biology, 65(5), 055011. Düzleştirme Katmanı ile görüntü matrislerinin vektör hâline getirilmesi. (2021, 14 Ocak). Erişim Adresi: https://medium.com/@tuncerergin/ convolutional-neural-network-convnet-yada-cnn-nedir-nasil-calisir-97a0f5d34cad
Ehrlich, Ruth Ann., Daly, Joan A. (2008). Patient Care in Radiography: With an introduction to medical imaging (Seventh Edition). MO: Elsevier. E
l-Baz, A. S., & Suri, J. S. (2019). Lung imaging and CADx. Boca Raton, FL: CRC Press/Taylor & Francis.
El-Baz, A., Beache, G. M., Gimel’farb, G., Suzuki, K., Okada, K., Elnakib, A., ... & Abdollahi, B. (2013). Computer-aided diagnosis systems for lung cancer: challenges and methodologies. International journal of biomedical imaging.
Ertel, W., Black, N., & Mast, F. (2017). Introduction to artificial intelligence. Cham, Switzerland: Springer. European Society of Radiology (ESR. (2015). ESR position paper on imaging biobanks. Insights into imaging, 6(4), 403-410.
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Toplam 76 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Bilgisayar Yazılımı
Bölüm	Derleme
Yazarlar	Furkan Atlan 0000-0003-1602-1941 İhsan Pençe 0000-0003-0734-3869
Yayımlanma Tarihi	29 Haziran 2021
Gönderilme Tarihi	20 Ekim 2020
Yayımlandığı Sayı	Yıl 2021 Cilt: 5 Sayı: 1

Kaynak Göster

APA	Atlan, F., & Pençe, İ. (2021). Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. Acta Infologica, 5(1), 207-230.
AMA	Atlan F, Pençe İ. Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. ACIN. Haziran 2021;5(1):207-230.
Chicago	Atlan, Furkan, ve İhsan Pençe. “Yapay Zekâ Ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”. Acta Infologica 5, sy. 1 (Haziran 2021): 207-30.
EndNote	Atlan F, Pençe İ (01 Haziran 2021) Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. Acta Infologica 5 1 207–230.
IEEE	F. Atlan ve İ. Pençe, “Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”, ACIN, c. 5, sy. 1, ss. 207–230, 2021.
ISNAD	Atlan, Furkan - Pençe, İhsan. “Yapay Zekâ Ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”. Acta Infologica 5/1 (Haziran 2021), 207-230.
JAMA	Atlan F, Pençe İ. Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. ACIN. 2021;5:207–230.
MLA	Atlan, Furkan ve İhsan Pençe. “Yapay Zekâ Ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış”. Acta Infologica, c. 5, sy. 1, 2021, ss. 207-30.
Vancouver	Atlan F, Pençe İ. Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış. ACIN. 2021;5(1):207-30.

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