35 papers with code • 1 benchmarks • 2 datasets
Multi-spectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain.
We report a time saving of 2. 8, 3. 0, 1. 9, 4. 4, and 8. 6 fold compared to other interactive segmentation techniques.
Results show that the segmentation accuracy of our method is similar to a system trained on non-encoded images, while considerably reducing the ability to recover subject identity.
Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation.
The segmentation quality is quantified using the Dice metric for a total of 27 anatomical structures.
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine.
In the current study, a compact 3D convolutional neural network (CNN), referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template.
Glioma is a common type of brain tumor, and accurate detection of it plays a vital role in the diagnosis and treatment process.
Brain magnetic resonance (MR) segmentation for hydrocephalus patients is considered as a challenging work.