A. Diaz et al., “Childhood-onset asthma in smokers. association between CT measures of airway size, lung function, and chronic airflow obstruction”, Annals of the American Thoracic Society, vol. 11, no. 9, pp. 1371–1378, 2014.
Publications
C
S. Kurugol, E. Bas, D. Erdogmus, J. Dy, G. Sharp, and D. Brooks, “Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images”, in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2011, pp. 3403–3406.
S. Kurugol, E. Bas, D. Erdogmus, J. Dy, G. Sharp, and D. Brooks, “Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images”, in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, IEEE, 2011, pp. 3403–3406.
S. Kurugol, E. Bas, D. Erdogmus, J. Dy, G. Sharp, and D. Brooks, “Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images”, in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, IEEE, 2011, pp. 3403–3406.
Kurugol, Diaz, Washko, and S. J. Estepar, “C37 COPD: WHAT IS NEW IN IMAGING?: Ranking Of Emphysema Patterns Into Monotonic Disease Progression Levels Using Local Density Histograms In Chest CT Scans”, American Journal of Respiratory and Critical Care Medicine, vol. 189, p. 1, 2014.
B
J. Coll-Font et al., “Bulk motion-compensated DCE-MRI for functional imaging of kidneys in newborns”, Journal of Magnetic Resonance Imaging, 2019.
A
M. Haghighi, S. Warfield, and S. Kurugol, “Automatic renal segmentation in DCE-MRI using convolutional neural networks”, in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, pp. 1534–1537.
M. Haghighi, S. K. Warfield, and S. Kurugol, “Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks”, in IEEE International Symposium on Biomedical Imaging (ISBI) , 2018.
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively.
S. Kurugol et al., “Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions”, Medical physics, vol. 42, no. 9, pp. 5467–5478, 2015.
S. Kurugol et al., “Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions”, Medical physics, vol. 42, no. 9, pp. 5467–5478, 2015.