4 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
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…