Search Results for author:
Found 8 papers, 8 papers with code
AeroPath: An airway segmentation benchmark dataset with challenging pathology
In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations.
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.
Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation
Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph.
Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images
To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumour segmentation.
Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding
For the 1178 lymph nodes with a short-axis diameter $\geq10$ mm, our best performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5, and a segmentation overlap of 80. 5%.
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).
