Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images
Fang, S.; Tward, D. J.
Show abstract
Large-scale brain mapping increasingly relies on integrating histological imaging datasets within standardized anatomical reference frameworks. To accomplish this, registration techniques are used to align imaging datasets between different modalities or to reference atlases. Deep learning approaches have emerged a fast and scalable framework for approaching this challenge, but such methods typically require large annotated datasets that are unavailable in this setting. To address this, we developed a framework for training a convolutional neural network for this task using entirely simulated data. We show that the same approach can be used for different species (mouse and marmoset), and we provide accuracy validation in terms of Dice and Hasudroff distance between anatomical regions in comparison to an alternative method. This approach has the potential to accelerate large or high throughput studies of brain anatomy.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Tensor Image Registration Library: Automated Deformable Registration of Stand-Alone Histology Images to Whole-Brain Post-Mortem MRI Data 97%
- Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases 95%
- Self-Supervised Natural Image Reconstruction and Large-Scale Semantic Classification from Brain Activity 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.