activity
20202023
most citedPreoperative brain tumor imaging: models and software for segmentation and standardized reporting

43 citations · 81 across the 6 of their papers we have counts for

collaborators

6 papers

physics.med-ph2023★ 2 cited

Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting

David Bouget, Demah Alsinan, Valeria Gaitan +4

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic…

eess.IV2023★ 3 cited

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Ragnhild Holden Helland, Alexandros Ferles, André Pedersen +30

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of resid…

physics.med-ph2022★ 7 cited

RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images

Bahareh Behboodi, Francois-Xavier Carton, Matthieu Chabanas +6

Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images play an essential role in surgical planning and resection of brain tumors. However, val…

eess.IV2022★ 43 cited

Preoperative brain tumor imaging: models and software for segmentation and standardized reporting

D. Bouget, A. Pedersen, A. S. Jakola +27

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the l…

eess.IV2021

Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms

David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey +2

Meningiomas are the most common type of primary brain tumor, accounting for approximately 30% of all brain tumors. A substantial number of these tumors are never surgically removed…

eess.IV2020★ 26 cited

Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture

David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey +3

Automatic and consistent meningioma segmentation in T1-weighted MRI volumes and corresponding volumetric assessment is of use for diagnosis, treatment planning, and tumor growth ev…