Beyin Bilgisayarlı Tomografi Anjiyografi Tarama Görüntülerinde Derin Öğrenme Tabanlı Otomatik Serebral Anevrizma Tespiti

Year 2024, EARLY VIEW, 1 - 1

Meltem Yavuz Çelikdemir , Ayhan Akbal

https://doi.org/10.2339/politeknik.1261854

Abstract

Serebral anevrizmalar, insan hayatını tehdit eden önemli bir hastalıktır. Bu anevrizmaların rüptüre olması beyin arterlerinde kanamalara sebep olmaktadır. Klinik olarak cerebral anevrizmaların tanısında bilgisayarlı tomografi anjiyografi yaygın olarak kullanılmaktadır. Bununla birlikte radyoloji uzmanlarının tomografi görüntülerini yorumlama hataları hastalar için hayati öneme sahiptir. Bu öneme binaen yapılan derin öğrenme tabanlı çalışmalar bu hataları minimum seviyede tutmaya yardımcı olmayı amaçlamaktadır. Bu amaç doğrultusunda yapılan bu çalışmada serebral anevrizmaların tespiti için BTA görüntüleri kullanılmıştır. BTA görüntü analizleri için ise Evrişimli Sinir Ağı aracılığı ile derin öğrenme metodolojisi tercih edilmiştir. Derin öğrenme sonucunda elde edilen eğitimin doğrulama doğruluğu %99.54, duyarlılığı %98, özgüllüğü %100, hassaslığı %100 ile yüksek orana sahiptir. Eğitim veri seti olarak, anevrizmalı hasta görüntüleri için 127 doğru pozitif, 1 yanlış negatif, anevrizmasız hasta görüntüleri için 89 doğru pozitif, 0 yanlış pozitif olarak sonuç vermiştir. Eğitilmiş ağda, harici test için 75 CTA görüntüde %86.6 gibi yüksek bir doğrulukla sonuçlar elde edildi. Test işleminde anevrizma tespit edilen bir görüntü için bölgesel boyut analizi yapılmıştır.

Keywords

Derin öğrenme, BTA-tarama görüntüsü, serebnal anevrizma

A Deep Learning Based on Automatic Cerebral Aneurysm Detection in Brain Computed Tomography Angiography Scan Images

Abstract

Cerebral aneurysms are an important disease that threatens human life. Rupture of these aneurysms causes hemorrhages in the cerebral arteries. Computed Tomography Angiography (CTA) is widely used in the clinical diagnosis of cerebral aneurysms. Interpretation errors by radiologists in examining these Computed Tomography (CT) images are vital for patients. Based on this importance, deep learning-based studies aim to help keep these errors to a minimum. For this purpose, CTA images were used to detect cerebral aneurysms in this study. For CTA image analysis, deep learning methodology was preferred through Convolutional Neural Network (CNN). The validation accuracy of the training obtained as a result of deep learningg has a high rate of validation with 99.54% accuracy, 100% sensitivity, 98.89% specificity and 100% precision. As a training dataset, it yielded 127 true positives and 1 false positive for patient images with aneurysm, 89 true negatives and 0 false negative for images of patients with non-aneurysms. In this trained network, results were obtained with a high accuracy of 86.6% on 75 CTA images for external test. Regional dimension analysis was also performed for an image with an aneurysm detected in the test process.

Keywords

Deep Learning, CTA-Scan Image, cerebnal aneurysm

References

• [1] Cinar C, Oran I. "Intrakraniyal Dissekan ve Travmatik Anevrizmalarda Tedavi,Türk Radyoloji Semin,10,115–27,(2022).
• [2] Li MH, Chen SW, Li YD, Chen YC, Cheng YS, Hu DJ, et al. "Prevalence of unruptured cerebral aneurysms in Chinese adults aged 35 to 75 years: A cross-sectional study", Ann Intern Med (2013).
• [3] Wei X, Jiang J, Cao W, Yu H, Deng H, Chen J, et al. Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography. Eur J Radiol, 149,110169,(2022).
• [4] Greving JP, Wermer MJH, Brown RD, Morita A, Juvela S, Yonekura M, et al. "Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies" Lancet Neurol, 13,59–66,(2014).
• [5] Mensah E, Pringle C, Roberts G, Gurusinghe N, Golash A, Alalade AF" Deep Learning in the Management of Intracranial Aneurysms and Cerebrovascular Diseases: A Review of the Current Literature", World Neurosurg, (2022).
• [6] Gu F, Wu X, Wu W, Wang Z, Yang X, Chen Z, et al. "Performance of deep learning in the detection of intracranial aneurysm: A systematic review and meta-analysis", Eur J Radiol , 155:110457,(2022).
• [7] Wang J, Ti L, Sun X, Yang R, Zhang N, Sun K. DSA "Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm". Scanning,1–6,(2022).
• [8] Brisman JL, Song JK, Newell DW. "Cerebral Aneurysms" N Engl J Med.,355,928–39,(2006).
• [9] Heit JJ, Honce JM, Yedavalli VS, Baccin CE, Tatit RT, Copeland K, et al. "RAPID Aneurysm: Artificial intelligence for unruptured cerebral aneurysm detection on CT angiography", J Stroke Cerebrovasc Dis,31:106690,(2022).
• [10] Silva MA, Patel J, Kavouridis V, Gallerani T, Beers A, Chang K, et al. "Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture", World Neurosurg,131,e46–51,(2019).
• [11] Buchlak QD, Milne MR, Seah J, Johnson A, Samarasinghe G, Hachey B, et al. "Charting the potential of brain computed tomography deep learning systems", J Clin Neurosci, (2022).
• [12] Duan H, Huang Y, Liu L, Dai H, Chen L, Zhou L. "Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks", Biomed Eng Online, 18:110,(2019).
• [13] Liao J, Liu L, Duan H, Huang Y, Zhou L, Chen L, et al. "Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation", JMIR Med Informatics, (2022).
• [14] Hu T, Yu J, Yang H, Ni W. "Segmentation of Intracranial Aneurysm Based on U-Net and BiConvGRU. Proc" - 14th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI (2021).
• [15] Mei Y jia, Hu R ting, Lin J, Xu H yu, Wu L ya, Li H peng, et al. "Diagnosis of Middle Cerebral Artery Stenosis Using Transcranial Doppler Images Based on Convolutional Neural Network", World Neurosurg (2022).
• [16] Dai X, Huang L, Qian Y, Xia S, Chong W, Liu J, et al. "Deep learning for automated cerebral aneurysm detection on computed tomography images", Int J Comput Assist Radiol Surg (2020).
• [17] Di Noto T, Marie G, Tourbier S, Alemán-Gómez Y, Esteban O, Saliou G, et al. "Towards Automated Brain Aneurysm Detection in TOF-MRA: Open Data, Weak Labels, and Anatomical Knowledge". Neuroinformatics, (2022).
• [18] Ou C, Qian Y, Chong W, Hou X, Zhang M, Zhang X, et al. "A deep learning–based automatic system for intracranial aneurysms diagnosis on three‐dimensional digital subtraction angiographic images", Med Phys (2022).
• [19] Ivantsits M, Goubergrits L, Kuhnigk JM, Huellebrand M, Bruening J, Kossen T, et al. "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge". Med Image Anal, (2022).
• [20] Sathish Kumar L, Hariharasitaraman S, Narayanasamy K, Thinakaran K, Mahalakshmi J, Pandimurugan V. "AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images", Mater. Today Proc., (2021).
• [21] Darici MB. "Performance Analysis of Combination of CNN-based Models with Adaboost Algorithm to Diagnose Covid-19 Disease", Politeknik Dergisi 26,179–90,(2023).
• [22] Biçer MB, Eliiyi U, Türsel Eliiyi D. "Deep Learning-based Classification of Breast Tumors using Raw Microwave Imaging Data", Journal of Polytechnic (2023).
• [23] Tümay M, Civelek Z, Teke M. Glakom ve Katarakt "Hastalığının Derin Öğrenme Modelleri ile Teşhisi", Journal of Polytechnic, (2023).
• [24] Akbulut H, Aslantaş V. "Evrişimli sinir ağı kullanarak çoklu-pozlamalı görüntü birleştirme", Gazi Üniversitesi Mühendislik Mimar Fakültesi Dergisi 38,1439–52,(2023).
• [25] Gupta K, Bajaj V. "Deep Learning Models-Based CT-Scan Image Classification for Automated Screening of COVID-19", SSRN Electron J, (2022).