7 citations · 15 across the 5 of their papers we have counts for
5 papers
The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data
Tobias Fechter, Ilias Sachpazidis, Dimos Baltas
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in inte…
Measuring breathing induced oesophageal motion and its dosimetric impact
Tobias Fechter, Sonja Adebahr, Anca-Ligia Grosu +1
Stereotactic body radiation therapy allows for a precise and accurate dose delivery. Organ motion during treatment bears the risk of undetected high dose healthy tissue exposure. A…
Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
Dejan Kostyszyn, Tobias Fechter, Nico Bartl +22
Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific me…
One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
Tobias Fechter, Dimos Baltas
Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising res…
A 3D fully convolutional neural network and a random walker to segment the esophagus in CT
Tobias Fechter, Sonja Adebahr, Dimos Baltas +3
Precise delineation of organs at risk (OAR) is a crucial task in radiotherapy treatment planning, which aims at delivering high dose to the tumour while sparing healthy tissues. In…