Mr. Simone Bendazzoli Profile

Simone Bendazzoli

at KTH Royal Institute of Technology

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 4 March 2022 Poster + Presentation + Paper

Estimation of the cross-sectional surface area of the waist of the nerve fiber layer at the optic nerve head

Konstancija Kisonaite, Zhaohua Yu, Faisal Raeme, Simone Bendazzoli, Chunliang Wang, Per Söderberg

Proceedings Volume 11941, 119410F (2022) https://doi.org/10.1117/12.2608073

KEYWORDS: Nerve, Optic nerve, Head, Evolutionary algorithms, Retina, Optical coherence tomography, Axons, Artificial intelligence, Visualization, Image segmentation

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Proceedings Article | 5 March 2021 Presentation + Paper

Fully automatic estimation of the waist of the nerve fiber layer at the optic nerve head angularly resolved

Irene Brusini, Gabriel Carizzo, Simone Bendazzoli, Chunliang Wang, Zhaohua Yu, Camilla Sandberg Melin, Konstancija Kisonaite, Faisal Raeme, Per Söderberg

Proceedings Volume 11623, 116231D (2021) https://doi.org/10.1117/12.2583562

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The present project aims at developing a fully automatic software for estimation of the waist of the nerve fiber layer in the Optic Nerve Head (ONH) angularly resolved in the frontal plane as a tool for morphometric monitoring of glaucoma. The waist of the nerve fiber layer is here defined as Pigment epithelium central limit –Inner limit of the retina – Minimal Distance, (PIMD). 3D representations of the ONH were collected with high resolution OCT in young not glaucomatous eyes and glaucomatous eyes. An improved tool for manual annotation was developed in Python. This tool was found user friendly and to provide sufficiently precise manual annotation. PIMD was automatically estimated with a software consisting of one AI model for detection of the inner limit of the retina and another AI model for localization of the Optic nerve head Pigment epithelium Central limit (OPCL). In the current project, the AI model for OPCL localization was retrained with new data manually annotated with the improved tool for manual annotation both in not glaucomatous eyes and in glaucomatous eyes. Finally, automatic annotations were compared to 3 annotations made by 3 independent annotators in an independent subset of both the not glaucomatous and the glaucomatous eyes. It was found that the fully automatic estimation of PIMD-angle overlapped the 3 manual annotators with small variation among the manual annotators. Considering interobserver variation, the improved tool for manual annotation provided less variation than our original annotation tool in not glaucomatous eyes suggesting that variation in glaucomatous eyes is due to variable pathological anatomy, difficult to annotate without error. The small relative variation in relation to the substantial overall loss of PIMD in the glaucomatous eyes compared to the not glaucomatous eyes suggests that our software for fully automatic estimation of PIMD-angle can now be implemented clinically for monitoring of glaucoma progression.

Proceedings Article | 15 March 2019 Paper

Automatic rat brain segmentation from MRI using statistical shape models and random forest

Simone Bendazzoli, Irene Brusini, Peter Damberg, Örjan Smedby, Leif Andersson, Chunliang Wang

Proceedings Volume 10949, 109492O (2019) https://doi.org/10.1117/12.2512409

KEYWORDS: Machine learning, Principal component analysis, Brain imaging, Image segmentation, Brain, Neuroimaging, Data modeling, Magnetic resonance imaging, Statistical modeling

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