43 citations · 107 across the 13 of their papers we have counts for
13 papers
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…
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Maren Høibø, André Pedersen, Vibeke Grotnes Dale +8
Digital pathology enables automatic analysis of histopathological sections using artificial intelligence (AI). Automatic evaluation could improve diagnostic efficiency and help fin…
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…
Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation
Vemund Fredriksen, Svein Ole M. Svele, André Pedersen +3
Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutiona…
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…
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…
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…
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…