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Found 7 papers, 3 papers with code
IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels
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 scanners, and the variety of image properties they produce.
The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data
In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields.
3D-OOCS: Learning Prostate Segmentation with Inductive Bias
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 protocols utilized by different scanners, and to the variety of image properties or artefacts they produce.
Measuring breathing induced oesophageal motion and its dosimetric impact
An organ very susceptible to high dose is the oesophagus.
Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa).
One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets.
A 3D fully convolutional neural network and a random walker to segment the esophagus in CT
These figures translate into a very good agreement with the reference contours and an increase in accuracy compared to other methods.
