Di Ieva A - Search Results

1

Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI.

Russo C, Liu S, Di Ieva A. Russo C, et al. Among authors: di ieva a. Med Biol Eng Comput. 2022 Jan;60(1):121-134. doi: 10.1007/s11517-021-02464-1. Epub 2021 Nov 2. Med Biol Eng Comput. 2022. PMID: 34729681

2

Cancer initiation and progression: an unsimplifiable complexity.

Grizzi F, Di Ieva A, Russo C, Frezza EE, Cobos E, Muzzio PC, Chiriva-Internati M. Grizzi F, et al. Among authors: di ieva a. Theor Biol Med Model. 2006 Oct 17;3:37. doi: 10.1186/1742-4682-3-37. Theor Biol Med Model. 2006. PMID: 17044918 Free PMC article.

3

Fractal dimension as a quantitator of the microvasculature of normal and adenomatous pituitary tissue.

Di Ieva A, Grizzi F, Ceva-Grimaldi G, Russo C, Gaetani P, Aimar E, Levi D, Pisano P, Tancioni F, Nicola G, Tschabitscher M, Dioguardi N, Baena RR. Di Ieva A, et al. J Anat. 2007 Nov;211(5):673-80. doi: 10.1111/j.1469-7580.2007.00804.x. Epub 2007 Sep 3. J Anat. 2007. PMID: 17784937 Free PMC article.

4

Deep learning for automated cerebral aneurysm detection on computed tomography images.

Dai X, Huang L, Qian Y, Xia S, Chong W, Liu J, Di Ieva A, Hou X, Ou C. Dai X, et al. Among authors: di ieva a. Int J Comput Assist Radiol Surg. 2020 Apr;15(4):715-723. doi: 10.1007/s11548-020-02121-2. Epub 2020 Feb 13. Int J Comput Assist Radiol Surg. 2020. PMID: 32056126

5

Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.

Jang K, Russo C, Di Ieva A. Jang K, et al. Among authors: di ieva a. Neuroradiology. 2020 Jul;62(7):771-790. doi: 10.1007/s00234-020-02403-1. Epub 2020 Apr 6. Neuroradiology. 2020. PMID: 32249351 Review.

6

Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning.

Liu S, Shah Z, Sav A, Russo C, Berkovsky S, Qian Y, Coiera E, Di Ieva A. Liu S, et al. Among authors: di ieva a. Sci Rep. 2020 May 7;10(1):7733. doi: 10.1038/s41598-020-64588-y. Sci Rep. 2020. PMID: 32382048 Free PMC article.

7

Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation.

Gao Y, Xiao X, Han B, Li G, Ning X, Wang D, Cai W, Kikinis R, Berkovsky S, Di Ieva A, Zhang L, Ji N, Liu S. Gao Y, et al. Among authors: di ieva a. JMIR Med Inform. 2020 Nov 17;8(11):e19805. doi: 10.2196/19805. JMIR Med Inform. 2020. PMID: 33200991 Free PMC article.

8

Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.

Di Ieva A, Russo C, Liu S, Jian A, Bai MY, Qian Y, Magnussen JS. Di Ieva A, et al. Neuroradiology. 2021 Aug;63(8):1253-1262. doi: 10.1007/s00234-021-02649-3. Epub 2021 Jan 26. Neuroradiology. 2021. PMID: 33501512

9

Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Jian A, Jang K, Manuguerra M, Liu S, Magnussen J, Di Ieva A. Jian A, et al. Among authors: di ieva a. Neurosurgery. 2021 Jun 15;89(1):31-44. doi: 10.1093/neuros/nyab103. Neurosurgery. 2021. PMID: 33826716

10

Brain volumetric and fractal analysis of synthetic MRI: A comparative study with conventional 3D T1-weighted images.

Liu S, Meng T, Russo C, Di Ieva A, Berkovsky S, Peng L, Dou W, Qian L. Liu S, et al. Among authors: di ieva a. Eur J Radiol. 2021 Aug;141:109782. doi: 10.1016/j.ejrad.2021.109782. Epub 2021 May 21. Eur J Radiol. 2021. PMID: 34049059

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