74 citations · 159 across the 14 of their papers we have counts for
14 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…
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations
Mathilde Faanes, Ragnhild Holden Helland, Ole Solheim +2
Automatic segmentation of brain tumors in intra-operative ultrasound (iUS) images could facilitate localization of tumor tissue during resection surgery. The lack of large annotate…
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…
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…
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…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
Code-free development and deployment of deep segmentation models for digital pathology
Henrik Sahlin Pettersen, Ilya Belevich, Elin Synnøve Røyset +5
Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the abi…
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…
FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology
André Pedersen, Marit Valla, Anna M. Bofin +3
Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs)…
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…
Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge
Yiming Xiao, Hassan Rivaz, Matthieu Chabanas +13
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets…