M
Mikael Agn
Researcher at Technical University of Denmark
Publications - 9
Citations - 297
Mikael Agn is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Radiation treatment planning & Generative model. The author has an hindex of 5, co-authored 9 publications receiving 219 citations. Previous affiliations of Mikael Agn include Copenhagen University Hospital.
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Book ChapterDOI
An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation
TL;DR: This paper proposes a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images and shows improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation without growcut.
Journal ArticleDOI
Role of Serotonin Transporter Changes in Depressive Responses to Sex-Steroid Hormone Manipulation: A Positron Emission Tomography Study
Vibe G. Frokjaer,Anja Pinborg,Klaus K. Holst,Agnete Overgaard,Susanne Henningsson,Maria Heede,Elisabeth Clare Larsen,Peter S. Jensen,Mikael Agn,Anna P. Nielsen,Dea S. Stenbæk,Sophie da Cunha-Bang,Szabolcs Lehel,Hartwig R. Siebner,Hartwig R. Siebner,Jens D. Mikkelsen,Claus Svarer,Gitte M. Knudsen +17 more
TL;DR: The data imply both serotonergic signaling and estradiol in the mechanisms by which sex-steroid hormone fluctuations provoke depressive symptoms and thus provide a rationale for future preventive strategies in high-risk groups.
Book ChapterDOI
Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape
TL;DR: A fully automated generative method for brain tumor segmentation in multi-modal magnetic resonance images that performs well compared to current state-of-the-art methods, while not being tied to any specific imaging protocol.
Journal ArticleDOI
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.
Mikael Agn,Per Munck af Rosenschöld,Oula Puonti,Michael Lundemann,Laura Mancini,Anastasia Papadaki,Steffi Thust,John Ashburner,Ian Law,Koen Van Leemput +9 more
TL;DR: In this paper, a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines is presented.
Proceedings ArticleDOI
A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients
TL;DR: The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape, and demonstrates the feasibility of the method on a manually delineated clinical data set of glioblastoma patients.