43 citations · 106 across the 11 of their papers we have counts for
11 papers
A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time points
Mathilde Gajda Faanes, David Bouget, Asgeir S. Jakola +11
T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) scans are important for diagnosis, treatment planning and monitoring of brain tumors. Depen…
Automatic and standardized surgical reporting for central nervous system tumors
David Bouget, Mathilde Gajda Faanes, Asgeir Store Jakola +17
Magnetic resonance (MR) imaging is essential for evaluating central nervous system (CNS) tumors, guiding surgical planning, treatment decisions, and assessing postoperative outcome…
AeroPath: An airway segmentation benchmark dataset with challenging pathology
Karen-Helene Støverud, David Bouget, Andre Pedersen +3
To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for…
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…
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…
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation
Javier Pérez de Frutos, André Pedersen, Egidijus Pelanis +5
Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. Methods: Differe…
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…
Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images
André Pedersen, Erik Smistad, Tor V. Rise +6
Histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic m…
Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding
David Bouget, André Pedersen, Johanna Vanel +2
As lung cancer evolves, the presence of enlarged and potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strat…
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…
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…