1 code implementation • 22 Nov 2023 • Maren Høibø, André Pedersen, Vibeke Grotnes Dale, Sissel Marie Berget, Borgny Ytterhus, Cecilia Lindskog, Elisabeth Wik, Lars A. Akslen, Ingerid Reinertsen, Erik Smistad, Marit Valla
In this study, we aimed to develop an AI model for segmentation of epithelial cells in sections from breast cancer.
1 code implementation • 28 Apr 2023 • David Bouget, Demah Alsinan, Valeria Gaitan, Ragnhild Holden Helland, André Pedersen, Ole Solheim, Ingerid Reinertsen
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 resonance (MR) scans.
1 code implementation • 18 Apr 2023 • Ragnhild Holden Helland, Alexandros Ferles, André Pedersen, Ivar Kommers, Hilko Ardon, Frederik Barkhof, Lorenzo Bello, Mitchel S. Berger, Tora Dunås, Marco Conti Nibali, Julia Furtner, Shawn Hervey-Jumper, Albert J. S. Idema, Barbara Kiesel, Rishi Nandoe Tewari, Emmanuel Mandonnet, Domenique M. J. Müller, Pierre A. Robe, Marco Rossi, Lisa M. Sagberg, Tommaso Sciortino, Tom Aalders, Michiel Wagemakers, Georg Widhalm, Marnix G. Witte, Aeilko H. Zwinderman, Paulina L. Majewska, Asgeir S. Jakola, Ole Solheim, Philip C. De Witt Hamer, Ingerid Reinertsen, Roelant S. Eijgelaar, David Bouget
The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection.
1 code implementation • 28 Nov 2022 • Javier Pérez de Frutos, André Pedersen, Egidijus Pelanis, David Bouget, Shanmugapriya Survarachakan, Thomas Langø, Ole-Jakob Elle, Frank Lindseth
Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph.
1 code implementation • 21 Dec 2021 • Vemund Fredriksen, Svein Ole M. Svele, André Pedersen, Thomas Langø, Gabriel Kiss, Frank Lindseth
Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel.
1 code implementation • 7 Dec 2021 • André Pedersen, Erik Smistad, Tor V. Rise, Vibeke G. Dale, Henrik S. Pettersen, Tor-Arne S. Nordmo, David Bouget, Ingerid Reinertsen, Marit Valla
To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumour segmentation.
2 code implementations • 16 Nov 2021 • Henrik Sahlin Pettersen, Ilya Belevich, Elin Synnøve Røyset, Erik Smistad, Eija Jokitalo, Ingerid Reinertsen, Ingunn Bakke, André Pedersen
Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions.
1 code implementation • 11 Feb 2021 • David Bouget, André Pedersen, Johanna Vanel, Haakon O. Leira, Thomas Langø
For the 1178 lymph nodes with a short-axis diameter $\geq10$ mm, our best performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5, and a segmentation overlap of 80. 5%.
1 code implementation • 19 Jan 2021 • David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey, Ole Solheim, Ingerid Reinertsen
A larger number of cases with meningiomas below 3ml might also be needed to improve the performance for the smallest tumors.
3 code implementations • 11 Nov 2020 • André Pedersen, Marit Valla, Anna M. Bofin, Javier Pérez de Frutos, Ingerid Reinertsen, Erik Smistad
It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results.
1 code implementation • 14 Oct 2020 • David Bouget, André Pedersen, Sayied Abdol Mohieb Hosainey, Johanna Vanel, Ole Solheim, Ingerid Reinertsen
We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net).