7 citations · 15 across the 10 of their papers we have counts for
10 papers
Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses
Nanna E. Wielenberg, Ilinca Popp, Oliver Blanck +6
Melanoma brain metastases (MBM) are common and spatially heterogeneous lesions, complicating cohort-level analyses due to anatomical variability and differing MRI protocols. We pro…
Segmentation of Prostate Tumour Volumes from PET Images is a Different Ball Game
Shrajan Bhandary, Dejan Kuhn, Zahra Babaiee +5
Accurate segmentation of prostate tumours from PET images presents a formidable challenge in medical image analysis. Despite considerable work and improvement in delineating organs…
Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data
Nanna E. Hartong, Ilias Sachpazidis, Oliver Blanck +9
Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinic…
IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels
Shrajan Bhandary, Zahra Babaiee, Dejan Kostyszyn +4
Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different s…
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…
3D-OOCS: Learning Prostate Segmentation with Inductive Bias
Shrajan Bhandary, Zahra Babaiee, Dejan Kostyszyn +4
Despite the great success of convolutional neural networks (CNN) in 3D medical image segmentation tasks, the methods currently in use are still not robust enough to the different p…
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…