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Found 10 papers, 5 papers with code
Expectation Maximization Pseudo Labels
In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation
Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL.
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels
The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations.
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net.
Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1. 5T MRI).
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans
We showed that networks optimized using only weak labels reflecting WMH volume generalized better for WMH volume prediction than networks optimized with voxel-wise segmentations of WMH.
Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data.
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space.
Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain.
