Search Results for author:
Found 6 papers, 2 papers with code
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
A 3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from the HCC tumor segmentation.
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context.
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Robust localisation and identification of vertebrae is essential for automated spine analysis.
Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy
Furthermore, we explored two variants of adversarial training schemes that incorporated the anatomical a priori knowledge into the Btrfly Net.
Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
