Добірка наукової літератури з теми "Aneurysm detection"
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Статті в журналах з теми "Aneurysm detection":
Franjić, Darjan, and Josip Mašković. "Value of 3D-DSA in the detection of intracranial aneurysms." Medicina Fluminensis 57, no. 3 (September 1, 2021): 260–68. http://dx.doi.org/10.21860/medflum2021_261187.
Nakagawa, Daichi, Yasunori Nagahama, Bruno A. Policeni, Madhavan L. Raghavan, Seth I. Dillard, Anna L. Schumacher, Srivats Sarathy, et al. "Accuracy of detecting enlargement of aneurysms using different MRI modalities and measurement protocols." Journal of Neurosurgery 130, no. 2 (February 2019): 559–65. http://dx.doi.org/10.3171/2017.9.jns171811.
Gunia, D. J., E. T. Ekvtimishvili, and G. Z. Basiladze. "Necessity of follow-up cerebral digital subtraction angiography after endovascular coiling or microsurgical cliping of ruptured intracranial aneurysms to exclude de novo or aneurysmal regrow and avoid its rupture: report of 2 cases." Endovascular Neuroradiology 27, no. 1 (June 13, 2019): 12–20. http://dx.doi.org/10.26683/2304-9359-2019-1(27)-12-20.
Huston, J., V. E. Torres, P. P. Sulivan, K. P. Offord, and D. O. Wiebers. "Value of magnetic resonance angiography for the detection of intracranial aneurysms in autosomal dominant polycystic kidney disease." Journal of the American Society of Nephrology 3, no. 12 (June 1993): 1871–77. http://dx.doi.org/10.1681/asn.v3121871.
Paralic, Martin, Kamil Zelenak, Patrik Kamencay, and Robert Hudec. "Automatic Approach for Brain Aneurysm Detection Using Convolutional Neural Networks." Applied Sciences 13, no. 24 (December 16, 2023): 13313. http://dx.doi.org/10.3390/app132413313.
Dupont, Stefan A., Giuseppe Lanzino, Eelco F. M. Wijdicks, and Alejandro A. Rabinstein. "The use of clinical and routine imaging data to differentiate between aneurysmal and nonaneurysmal subarachnoid hemorrhage prior to angiography." Journal of Neurosurgery 113, no. 4 (October 2010): 790–94. http://dx.doi.org/10.3171/2010.4.jns091932.
Ajiboye, Norman, Nohra Chalouhi, Robert M. Starke, Mario Zanaty, and Rodney Bell. "Unruptured Cerebral Aneurysms: Evaluation and Management." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/954954.
Al Kasab, Sami, Daichi Nakagawa, Mario Zanaty, Girish Bathla, Bruno Policeni, Neetu Soni, Lauren Allan, et al. "In vitro accuracy and inter-observer reliability of CT angiography in detecting intracranial aneurysm enlargement." Journal of NeuroInterventional Surgery 11, no. 10 (March 6, 2019): 1015–18. http://dx.doi.org/10.1136/neurintsurg-2019-014737.
Imaizumi, Yohichi, Tohru Mizutani, Katsuyoshi Shimizu, Yosuke Sato, and Junichi Taguchi. "Detection rates and sites of unruptured intracranial aneurysms according to sex and age: an analysis of MR angiography–based brain examinations of 4070 healthy Japanese adults." Journal of Neurosurgery 130, no. 2 (February 2019): 573–78. http://dx.doi.org/10.3171/2017.9.jns171191.
Kizilkilic, Osman, Eldeniz Huseynov, Sedat G. Kandemirli, Naci Kocer, and Civan Islak. "Detection of wall and neck calcification of unruptured intracranial aneurysms with flat-detector computed tomography." Interventional Neuroradiology 22, no. 3 (February 2, 2016): 293–98. http://dx.doi.org/10.1177/1591019915626591.
Дисертації з теми "Aneurysm detection":
Wells, Catherine E. "Abdominal Aortic Aneurysm detection by common femoral artery Doppler ultrasound waveform analysis." Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/54725/.
Yuk, Jongtae. "Hemorrhage and aortic aneurysm detection in the abdomen using 3D ultrasound imaging /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/5882.
Maroney, Roy Thomas. "Missed opportunities for the detection of abdominal aortic aneurysms : a retrospective study of eighteen patients presenting with a ruptured or acute symptomatic abdominal aortic aneurysm." Master's thesis, University of Cape Town, 1997. http://hdl.handle.net/11427/25566.
BHADRI, PRASHANT R. "DEVELOPMENT OF AN INTEGRATED SOFTWARE/HARDWARE PLATFORM FOR THE DETECTION OF CEREBRAL ANEURYSM BY QUANTIFYING BILIRUBIN IN CEREBRAL SPINAL FLUID." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1126815429.
López-Linares, Karen. "Image analysis and deep learning to support endovascular repair of abdominal aortic aneurysms." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/667102.
El aneurisma de aorta abdominal (AAA) es una dilatación focal de la aorta que puede provocar su ruptura. El tratamiento habitual es la reparación endovascular (EVAR), que conlleva un seguimiento postoperatorio de por vida en base a imágenes de angiografía por tomografía computarizada (CTA) para detectar posibles complicaciones. Esta tesis establece la base para el análisis inteligente de imágenes CTA para apoyar el seguimiento postoperatorio de los AAA, proporcionando a los profesionales médicos información valiosa para predecir el comportamiento del aneurisma. Primero, se han desarrollado algoritmos de segmentación de AAA a partir de CTA preoperatorias y postoperatorias, basados en redes neuronales convolucionales (CNN). Inicialmente, se han propuesto CNNs 2D para la detección y la segmentación de AAAs. Posteriormente, el algoritmo de segmentación se ha extendido a 3D para mejorar su precisión, ya que ésta es la base para un buen seguimiento. Permite medir el volumen del aneurisma, que se considera un mejor indicador de riesgo de ruptura del AAA que la aproximación actual en base a su diámetro. Además, permite realizar análisis más complejos de la morfología y las deformaciones del AAA. Una vez obtenida la segmentación, se ha propuesto una metodología para el registro de series de CTA postoperatorias y el subsiguiente análisis biomecánico de las deformaciones del aneurisma. Dichas deformaciones se han cuantificado mediante descriptores de imagen y se han correlacionado con el pronóstico del paciente a largo plazo. Los descriptores extraídos establecen la base para el desarrollo de futuros biomarcadores de imagen que puedan ser utilizados en la práctica clínica para evaluar el pronóstico del paciente y para dar soporte al médico en sus decisiones tras una intervención EVAR. Por último, la experiencia adquirida en la tesis ha permitido aplicar algunas de las tecnologías para la resolución de problemas de segmentación complejos en otros ámbitos médicos, como la segmentación del músculo pectoral en mamografías o la segmentación de la arteria pulmonar en CTA. Actualmente, se está llevando a cabo la validación del algoritmo de segmentación de AAA 3D propuesto en esta tesis, con el objetivo de integrarlo en un producto comercial.
Wang, Yan. "Etude de la méthode de Boltzmann sur réseau pour la segmentation d'anévrismes cérébraux." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0078/document.
Cerebral aneurysm is a fragile area on the wall of a blood vessel in the brain, which can rupture and cause major bleeding and cerebrovascular accident. The segmentation of cerebral aneurysm is a primordial step for diagnosis assistance, treatment and surgery planning. Unfortunately, manual segmentation is still an important part in clinical angiography but has become a burden given the huge amount of data generated by medical imaging systems. Automatic image segmentation techniques provides an essential way to easy and speed up clinical examinations, reduce the amount of manual interaction and lower inter operator variability. The main purpose of this PhD work is to develop automatic methods for cerebral aneurysm segmentation and measurement. The present work consists of three main parts. The first part deals with giant aneurysm segmentation containing lumen and thrombus. The methodology consists of first extracting the lumen and thrombus using a two-step procedure based on the LBM, and then refining the shape of the thrombus using level set technique. In this part the proposed method is also compared with manual segmentation, demonstrating its good segmentation accuracy. The second part concerns a LBM approach to vessel segmentation in 2D+t images and to cerebral aneurysm segmentation in 3D medical images through introducing a LBM D3Q27 model, which allows achieving a good segmentation and high robustness to noise. The last part investigates a true 4D segmentation model by considering the 3D+t data as a 4D hypervolume and using a D4Q81 lattice in LBM where time is considered in the same manner as for other three dimensions for the definition of particle moving directions in the LBM model
White, P. M. "The detection of intracranial aneurysms by non-invasive imaging methods and the epidemiology of aneurysmal subarachnoid haemorrhage within the Scottish population." Thesis, University of Edinburgh, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.663704.
Assis, Youssef. "Détection des anévrismes intracrâniens par apprentissage profond." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0012.
Intracranial aneurysms are local dilatations of cerebral blood vessels, presenting a significant risk of rupture, which can lead to serious consequences. Early detection of unruptured aneurysms is therefore crucial to prevent potentially fatal complications. However, analyzing medical images to locate these aneurysms is a complex and time-consuming task, requiring time and expertise, and yet remains prone to errors in interpretation. Faced with these challenges, this thesis explores automated methods for the detection of aneurysms, aiming to facilitate the work of radiologists and improve diagnostic efficiency. Our approach focuses on the use of artificial intelligence techniques, particularly deep neural networks, for the detection of aneurysms from time-of-flight magnetic resonance angiography (TOF-MRA) images. Our research work is centered around several main axes. Firstly, due to the scarcity of training data in the medical field, we adopt a rapid, although approximate, annotation method to facilitate data collection. Furthermore, we propose a strategy based on small patches. In association with data synthesis, the samples are multiplied in the training database. By selecting the samples, their distribution is adjusted to facilitate optimization. Secondly, for the automated detection of aneurysms, we investigate various neural network architectures. An initial approach explores image segmentation networks. Then, we propose an innovative architecture inspired by object detection methods. These architectures, especially the latter, lead to competitive results, particularly in terms of sensitivity compared to experts. Thirdly, beyond the detection of aneurysms, we extend our model to estimate the pose of aneurysms in 3D images. This can greatly facilitate their analysis and interpretation in reformatted cross-sectional plans. A thorough evaluation of the proposed models is systematically carried out, including ablation studies, the use of metrics adapted to the problem of detection, and evaluations conducted by clinical experts, allowing us to assess their potential effectiveness for clinical use. In particular, we highlight the issues related to uncertainty in the annotation of existing databases
Yang, Guang. "Detection of micro-aneurysms in low-resolution color retinal images." Mémoire, [S.l. : s.n.], 2001. http://savoirs.usherbrooke.ca/handle/11143/4546.
Nikravanshalmani, Alireza. "Computer aided detection and segmentation of intracranial aneurysms in CT angiography." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/22974/.
Книги з теми "Aneurysm detection":
Hennemuth, Anja, Leonid Goubergrits, Matthias Ivantsits, and Jan-Martin Kuhnigk, eds. Cerebral Aneurysm Detection. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5.
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Частини книг з теми "Aneurysm detection":
Jain, Kartik. "CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamics." In Cerebral Aneurysm Detection, 75–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_8.
Jia, Yizhuan, Weibin Liao, Yi Lv, Ziyu Su, Jiaqi Dou, Zhongwei Sun, and Xuesong Li. "Detect and Identify Aneurysms Based on Adjusted 3D Attention UNet." In Cerebral Aneurysm Detection, 39–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_4.
Ivantsits, Matthias, Jan-Martin Kuhnigk, Markus Huellebrand, Titus Kuehne, and Anja Hennemuth. "Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection." In Cerebral Aneurysm Detection, 31–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_3.
Ma, Jun, and Ziwei Nie. "Exploring Large Context for Cerebral Aneurysm Segmentation." In Cerebral Aneurysm Detection, 68–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_7.
Ivantsits, Matthias, Leonid Goubergrits, Jan-Martin Kuhnigk, Markus Huellebrand, Jan Brüning, Tabea Kossen, Boris Pfahringer, et al. "Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)." In Cerebral Aneurysm Detection, 3–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_1.
Ivantsits, Matthias, Leonid Goubergrits, Jan Brüning, Andreas Spuler, and Anja Hennemuth. "Intracranial Aneurysm Rupture Prediction with Computational Fluid Dynamics Point Clouds." In Cerebral Aneurysm Detection, 104–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_11.
Spuler, Andreas, and Leonid Goubergrits. "CADA: Clinical Background and Motivation." In Cerebral Aneurysm Detection, 21–28. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_2.
Shit, Suprosanna, Ivan Ezhov, Johannes C. Paetzold, and Bjoern Menze. "A$$\nu $$-Net: Automatic Detection and Segmentation of Aneurysm." In Cerebral Aneurysm Detection, 51–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_5.
Su, Ziyu, Yizhuan Jia, Weibin Liao, Yi Lv, Jiaqi Dou, Zhongwei Sun, and Xuesong Li. "3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation Challenge." In Cerebral Aneurysm Detection, 58–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_6.
Ivantsits, Matthias, Markus Huellebrand, Sebastian Kelle, Titus Kuehne, and Anja Hennemuth. "Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning." In Cerebral Aneurysm Detection, 93–103. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72862-5_10.
Тези доповідей конференцій з теми "Aneurysm detection":
Byrne, Greg, Fernando Mut, and Juan R. Cebral. "Using Vortex Coreline Detection to Characterize Aneurysmal Flow Activity." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80209.
DeLeo, Michael J., Matthew J. Gounis, Bo Hong, Ronn Walvick, John Chetley Ford, Ajay K. Wakhloo, and Alexei A. Bogdanov. "Magnetic Resonance Detection of Inflammation in Elastase-Induced Aneurysms." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192608.
Tsai, William W., O¨mer Savas, Duncan Maitland, Jason Ortega, Ward Small, Thomas S. Wilson, and David Saloner. "Experimental Study of the Vascular Dynamics of a Saccular Basilar Aneurysm." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14662.
Hentschke, Clemens M., Klaus D. Tonnies, Oliver Beuing, and Rosa Nickl. "A new feature for automatic aneurysm detection." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235669.
Bindhya, P. S., R. Chitra, and V. S. Bibin Raj. "Sparse auto-encoder based micro-aneurysm detection." In INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INNOVATIVE TECHNOLOGIES IN AGRICULTURE”. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0170568.
Real, Eusebio, Jose Fernando Val-Bernal, Alejandro Ponton, Marta Calvo Diez, Marta Mayorga, Jose Manuel Revuelta, Jose Miguel Lopez-Higuera, and Olga M. Conde. "OCT for anomaly detection in aortic aneurysm resection." In 2014 IEEE Sensors. IEEE, 2014. http://dx.doi.org/10.1109/icsens.2014.6985094.
Hentschke, Clemens M., Oliver Beuing, Rosa Nickl, and Klaus D. Tonnies. "Automatic cerebral aneurysm detection in multimodal angiographic images." In 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (2011 NSS/MIC). IEEE, 2011. http://dx.doi.org/10.1109/nssmic.2011.6152566.
Le, Nam H., Edgar A. Samaniego, Ashrita Raghuram, Sebastian Sanchez, Honghai Zhang, and Milan Sonka. "Semi-automated intracranial aneurysm segmentation and neck detection." In Image Processing, edited by Ivana Išgum and Olivier Colliot. SPIE, 2022. http://dx.doi.org/10.1117/12.2613145.
Watton, Paul N., Marc Homer, Justin Penrose, Harry Thompson, Haoyu Chen, Alisa Selimovic, and Yiannis Ventikos. "Patient-Specific Modelling of Intracranial Aneurysm Evolution." In ASME 2011 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2011. http://dx.doi.org/10.1115/sbc2011-53223.
Spiclin, Ziga, Ziga Bizjak, Tim Jerman, Boštjan Likar, Franjo Pernuš, and Aichi Chien. "Registration based detection and quantification of intracranial aneurysm growth." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512781.
Звіти організацій з теми "Aneurysm detection":
Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, Ho-Ren Liu, Kuan-Ting Wu, Huan-Yu Hsu, Hao-Neng Fu, Yung-Tsai Lee, Wei-Hsian Yin, and Yu-Te Wu. The Role of Deep Learning in Aortic Aneurysm Segmentation and Detection from CT Scans: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2024. http://dx.doi.org/10.37766/inplasy2024.3.0126.