Search Results for author:
Found 6 papers, 4 papers with code
Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models
Accurately assessing tumor removal is paramount in the management of glioblastoma.
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans.
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks
The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection.
RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images
The proposed dataset contains tumor tissues and resection cavity annotations of the iUS images.
Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms
A larger number of cases with meningiomas below 3ml might also be needed to improve the performance for the smallest tumors.
Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture
We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net).
