no code implementations • 16 Aug 2023 • Denis Kutnár, Ivan R Vogelius, Katrin Elisabet Håkansson, Jens Petersen, Jeppe Friborg, Lena Specht, Mogens Bernsdorf, Anita Gothelf, Claus Kristensen, Abraham George Smith
We investigated the extent to which a Convolutional neural network (CNN) is able to predict LRR volumes based on pre-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) scans in HNSCC patients and thus the potential to identify biological high risk volumes using CNNs.
1 code implementation • 10 Apr 2023 • Abraham George Smith, Denis Kutnár, Ivan Richter Vogelius, Sune Darkner, Jens Petersen
Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest.
1 code implementation • 22 Jun 2021 • Abraham George Smith, Jens Petersen, Cynthia Terrones-Campos, Anne Kiil Berthelsen, Nora Jarrett Forbes, Sune Darkner, Lena Specht, Ivan Richter Vogelius
Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data.
1 code implementation • 28 Feb 2019 • Abraham George Smith, Jens Petersen, Raghavendra Selvan, Camilla Ruø Rasmussen
We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0. 9748 and an $r^2$ of 0. 9217.