Our Publications
Peer-reviewed papers, books and book chapters advancing computational neurosurgery and brain imaging science.
Our most impactful work
These publications represent the lab's core scientific contributions, from AI-driven tumour segmentation and spectroscopic fingerprinting to fluorescence-guided surgery and the Declaration of Sydney.
Selected recent papers
Browse, filter, and read peer-reviewed articles, conference proceedings, and book chapters and more.
Books and book chapters
Comprehensive monographs and invited chapters authored by lab members, covering computational approaches to neurosurgical disease.
Computational Neurosurgery
Di Ieva, A., Suero Molina, E., Liu, S., & Russo, C. (Eds.) (2024). Computational neurosurgery. (Advances in Experimental Medicine and Biology; Vol. 1462). Springer. https://doi.org/10.1007/978-3-031-64892-2
Computational Neuroscience
Di Ieva, A., Davidson, J. (Eds)(2026). Computational neuroscience. Springer. https://link.springer.com/book/9781071654392
The Fractal Geometry of the Brain
Di Ieva, A., (2024). The Fractal Geometry of the Brain. Springer. https://link.springer.com/book/10.1007/978-3-031-47606-8
The Fractal Geometry of the Brain
Di Ieva A. (2016). The Fractal Geometry of the Brain. Springer. https://link.springer.com/book/10.1007/978-1-4939-3995-4?page=2
Computational Neurosurgery - Chinese Translation
Di Ieva, A., Suero Molina, E., Liu, S., & Russo, C. (Eds.) (2026). Computational neurosurgery. Springer.
The fractal geometry of the brain: an overview
Di Ieva A.. The fractal geometry of the brain: an overview. The fractal geometry of the brain. 2024;:3--13.
Neurosurgery, explainable AI, and legal liability
Matulionyte R., Suero Molina E., Di Ieva A.. Neurosurgery, explainable AI, and legal liability. Computational neurosurgery. 2024;:543--553.
Meta-transfer learning for brain tumor segmentation: within and beyond glioma
Yan S., Liu S., Di Ieva A., Pagnucco M., Song Y.. Meta-transfer learning for brain tumor segmentation: within and beyond glioma. Computational neurosurgery. 2024;:221--230.
Machine and deep learning in hyperspectral fluorescence-guided brain tumor surgery
Suero Molina E., Black D., Xie A., Gill J., Di Ieva A., Stummer W.. Machine and deep learning in hyperspectral fluorescence-guided brain tumor surgery. Computational neurosurgery. 2024;:245--264.
Large language models in neurosurgery
Di Ieva A., Stewart C., Suero Molina E.. Large language models in neurosurgery. Computational neurosurgery. 2024;:177--198.
Fractals, pattern recognition, memetics, and AI: a personal journal in the computational neurosurgery
Di Ieva A.. Fractals, pattern recognition, memetics, and AI: a personal journal in the computational neurosurgery. The fractal geometry of the brain. 2024;:273--283.
Fractals in the neurosciences: a translational geographical approach
Andronache I., Peptenatu D., Ahammer H., Radulovic M., Djuričić G.J., Jelinek H.F., Russo C., Di Ieva A.. Fractals in the neurosciences: a translational geographical approach. The fractal geometry of the brain. 2024;:953--981.
Fractals in neuroimaging
Lahmiri S., Boukadoum M., Di Ieva A.. Fractals in neuroimaging. The fractal geometry of the brain. 2024;:429--444.
Fractals in neuroanatomy and basic neurosciences: an overview
Di Ieva A.. Fractals in neuroanatomy and basic neurosciences: an overview. The fractal geometry of the brain. 2024;:141--147.
Fractal-based analysis of arteriovenous malformations (AVMs)
Di Ieva A., Reishofer G.. Fractal-based analysis of arteriovenous malformations (AVMs). The fractal geometry of the brain. 2024;:413--428.
Fractal-based analysis of histological features of brain tumors
Al-Kadi O.S., Di Ieva A.. Fractal-based analysis of histological features of brain tumors. The fractal geometry of the brain. 2024;:501--524.
Fractal time series: background, estimation methods, and performances
Porcaro C., Moaveninejad S., D’Onofrio V., DiIeva A.. Fractal time series: background, estimation methods, and performances. The fractal geometry of the brain. 2024;:95--137.
Fractal geometry meets computational intelligence: future perspectives
Livi L., Sadeghian A., Di Ieva A.. Fractal geometry meets computational intelligence: future perspectives. The fractal geometry of the brain. 2024;:983--997.
Fractal dimension studies of the brain shape in aging and neurodegenerative diseases
Davidson J.M., Zhang L., Yue G.H., Di Ieva A.. Fractal dimension studies of the brain shape in aging and neurodegenerative diseases. The fractal geometry of the brain. 2024;:329--363.
Fractal dimension analysis in neurological disorders: an overview
Díaz Beltrán L., Madan C.R., Finke C., Krohn S., Di Ieva A., Esteban F.J.. Fractal dimension analysis in neurological disorders: an overview. The fractal geometry of the brain. 2024;:313--328.
Fractal analysis in clinical neurosciences: an overview
Di Ieva A.. Fractal analysis in clinical neurosciences: an overview. The fractal geometry of the brain. 2024;:261--271.
Declaration of computational neurosurgery
Di Ieva A., Suero Molina E., Somerville M.A., Beheshti A., Staartjes V.E., Serra C., Theodore N., Elliott J.M., Wesselink E.O., Russo C., Pilitsis J.G., Bennett C.C., Wu S., Hammond F.M., Lozano A.M., Cusimano M.D., Davidson J.M., Castellano J.F., Okonkwo D.O., Arefan D., Lee C., Zanier O., Da Mutten R., Matula C., Rutka J.T., Pease M., Liu S., Stummer W., Matulionyte R., Yang H., Yuwen C., Cheng X., Fan H., Wang X., Ge Z., Cepeda S., Sheehan J.P., Yang J.Y.M., Hamer R.P., Cohen-Gadol A., Hansford J.R., Savage G., Sowman P.F., Stewart C., Kateb B., Sherif C., Perperidis A., Guller A., Hanft S., D’Amico R.S., Sav A., Cong C., Song Y., Nicolosi F., Wiedmann M.K.H., Barone D.G., Noorani I., Magnussen J., Krieg S.M., Meling T.R., De Ridder D., Lawton M.T., Rosenfeld J.V.. Declaration of computational neurosurgery. Computational neurosurgery. 2024;:11--20.
Cross-Modality Synthesis of T1c MRI from Non-contrast Images Using GANs: Implications for Brain Tumor Research
Tabassum M., Rana P., Suero Molina E., Di Ieva A., Liu S.. Cross-Modality Synthesis of T1c MRI from Non-contrast Images Using GANs: Implications for Brain Tumor Research. Artificial Intelligence in Medicine. 2024;:60–69.
Computational neurosurgery: Foundation
Di Ieva A., Suero Molina E., Liu S., Russo C.. Computational neurosurgery: Foundation. Computational neurosurgery. 2024;:1--8.
Computational fractal-based neurosurgery
Di Ieva A., Davidson J.M., Russo C.. Computational fractal-based neurosurgery. Computational neurosurgery. 2024;:97--105.
Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology
Di Ieva A.. Computational fractal-based analysis of MR susceptibility-weighted imaging (SWI) in neuro-oncology and neurotraumatology. The fractal geometry of the brain. 2024;:445--468.
Computational and translational fractal-based analysis in the translational neurosciences: an overview
Di Ieva A.. Computational and translational fractal-based analysis in the translational neurosciences: an overview. The fractal geometry of the brain. 2024;:781--793.
Computational fractal-based analysis of brain tumor microvascular networks
Di Ieva A., Al-Kadi O.S.. Computational fractal-based analysis of brain tumor microvascular networks. The fractal geometry of the brain. 2024;:525--544.
Artificial intelligence, radiomics, and computational modeling in skull base surgery
Suero Molina E., Di Ieva A.. Artificial intelligence, radiomics, and computational modeling in skull base surgery. Computational neurosurgery. 2024;:265--283.
Artificial intelligence in brain tumors
Suero Molina E., Azemi G., Russo C., Liu S., Di Ieva A.. Artificial intelligence in brain tumors. Computational neurosurgery. 2024;:201--220.
Artificial intelligence methods
Liu S., Russo C., Suero Molina E., Di Ieva A.. Artificial intelligence methods. Computational neurosurgery. 2024;:21--38.
Analyzing eye paths using fractals
Newport R.A., Liu S., Di Ieva A.. Analyzing eye paths using fractals. The fractal geometry of the brain. 2024;:827--848.
An experimental verification of neurophysics treatment by executing a multifractal analysis of surface electromyography signals on trapezius, abdominals, and adductor muscles in athletes
Rinzivillo C., Kaleagasioglu F., Casciaro F., Scoppa F., Marvulli R., Ware K., Conte E., Di Ieva A.. An experimental verification of neurophysics treatment by executing a multifractal analysis of surface electromyography signals on trapezius, abdominals, and adductor muscles in athletes. Complexity science in human change. 2024;:81--117.
Robotics for approaches to the anterior cranial fossa
Anokwute M.C., Christodoulides A., Campbell R.G., Harvey R.J., Ieva A.D.. Robotics for approaches to the anterior cranial fossa. Robotics in skull-base surgery. 2023;:35--52.
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction
Russo C., Liu S., Ieva A.D.. Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2021;:295--306.
Publications
How I do it: 3D exoscopic endoscope-assisted microvascular decompression
Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis
AI-augmented multidisciplinary teams: hype or hope?
Transcranial colour duplex and central aortic pressure measurements in the management of cerebral arteriovenous malformations: a pilot study using non-invasive measures
Background: Following removal of an arteriovenous malformation of the brain (bAVM) the redistribution of blood can impose several clinically challenging issues including intracranial haemorrhage and arteriovenous capillary hypertensive syndrome. The underlying mechanism of such complications remains controversial, although control of blood pressure has been recognised as an integral component in haemorrhage prevention. Serial daily non-invasive monitoring for patients in this instance would be beneficial in improving management. Transcranial colour duplex (TCD) is a potential technique for providing pressure measurements and real-time, dynamic, haemodynamic spectra. Aim: To establish whether blood outflow velocity in the middle cerebral vein (MCV) can be quantified in the days following bAVM resection and whether values differ from other types of intracranial surgery. Methods: Blood pressure and TCD of 13 patients (aged 46±19 y, 7 female) having bAVMs resected and 7 patients having other intracranial surgeries (control group, aged 48±15 y, 6 female) were studied for days 1 to 3 following surgery. Ultrasound via the transtemporal window was used to obtain diameter, as well as peak and end-diastolic velocity of the MCV. Brachial blood pressure was also obtained using an automatic oscillometric blood pressure monitor. Results: Systolic (bAVM 96±2 mmHg, control 89±10 mmHg; P=0.68) and diastolic blood pressure (bAVM 55±2 mmHg, control 48±7 mmHg; P=0.92) did not differ between the groups. MCV peak systolic velocity was greater in the bAVM group (34±20 cm/s, controls 20±9 cm/s; P=0.049). The control group had peak systolic velocities ranging from 9 to 32 cm/s. Peak systolic velocity in the bAVM group varied from 9 to 98 cm/s. End diastolic velocity (bAVM 13±11 cm/s, control 13±6 cm/s; P=0.89) and diameters (bAVM 37±19 mm, control 26±7 mm; P=0.16) did not differ between the groups. Two of the bAVM patients that had sustained high MCV peak velocities had a post-operative haemorrhage. Conclusions: Unusually elevated blood flow velocities and diameters were observed in the MCV of patients following bAVM resection. Findings on this small data set provide insight into plausible vessel remodelling and elucidate a post-operative time frame when vessels may have impaired autoregulation of cerebral blood flow.
The Fractal Geometry of the Brain
Reviews the most intriguing applications of fractal analysis in neuroscience with a focus on current and future potential, limits, advantages, and disadvantages. Will bring an understanding of fractals to clinicians and researchers also if they do not have a mathematical background, and will serve as a good tool for teaching the translational applications of computational models to students and scholars of different disciplines. This comprehensive collection is organized in four parts: (1) Basics of fractal analysis; (2) Applications of fractals to the basic neurosciences; (3) Applications of fractals to the clinical neurosciences; (4) Analysis software, modeling and methodology.