The brain is a highly self-organized and extremely complex system that consists of numerous neural networks. Thus, the investigation of neural networks is central to understanding brain functions. With the recent advances in imaging techniques, imaging data can be generated from the brain at different scales, e.g., macro-, micro- or mesoscale, and from numerous modalities, e.g., electron microscopy (EM) images, light microscopy (LM) images and diffusion magnetic resonance imaging (dMRI). To study brain functions from the enormous amounts of imaging data, image analysis has been one of the essential branches. For example, digitally reconstructing the neural networks from images. Furthermore, to analyze the property of brain networks from the reconstructed data, it is also important to develop geometry analysis methods.
Although there are lots of studies in the literature that focused on image and geometry analysis for brain informatics, there are still many challenges in this field to be taken on. For example, how to automatically obtain the morphological representations of neuronal structures from whole brain images and how to correct the geometrical errors in the automated reconstruction results are still open problems. This Research Topic aims to gather the latest research addressing different challenges of image and geometry analysis for brain informatics, including neuron reconstruction, image segmentation, fiber tracking, geometry measures and so on. We welcome high-quality submissions of article types of original research.
Contributions to this Research Topic may address (but are not limited to) the following aspects of image and geometry analysis for brain informatics:
- Image segmentation for brain imaging data
- Neuron reconstruction and fiber tracking
- Geometry measures design and geometry analysis for reconstruction results
- Efficient representations of neuron morphology
- Classification and retrieval of neuronal data using geometry-based approaches
- Image registration, super-resolution and resampling for brain imaging data
- Brain imaging data transformation/synthesis
Zhi Zhou Ph.D. is a software engineer in Microsoft Corporation, a commercial producer of computer software, consumer electronics, personal computers, and related services. This should not pose any conflict for this project, as he will maintain objectivity.
The brain is a highly self-organized and extremely complex system that consists of numerous neural networks. Thus, the investigation of neural networks is central to understanding brain functions. With the recent advances in imaging techniques, imaging data can be generated from the brain at different scales, e.g., macro-, micro- or mesoscale, and from numerous modalities, e.g., electron microscopy (EM) images, light microscopy (LM) images and diffusion magnetic resonance imaging (dMRI). To study brain functions from the enormous amounts of imaging data, image analysis has been one of the essential branches. For example, digitally reconstructing the neural networks from images. Furthermore, to analyze the property of brain networks from the reconstructed data, it is also important to develop geometry analysis methods.
Although there are lots of studies in the literature that focused on image and geometry analysis for brain informatics, there are still many challenges in this field to be taken on. For example, how to automatically obtain the morphological representations of neuronal structures from whole brain images and how to correct the geometrical errors in the automated reconstruction results are still open problems. This Research Topic aims to gather the latest research addressing different challenges of image and geometry analysis for brain informatics, including neuron reconstruction, image segmentation, fiber tracking, geometry measures and so on. We welcome high-quality submissions of article types of original research.
Contributions to this Research Topic may address (but are not limited to) the following aspects of image and geometry analysis for brain informatics:
- Image segmentation for brain imaging data
- Neuron reconstruction and fiber tracking
- Geometry measures design and geometry analysis for reconstruction results
- Efficient representations of neuron morphology
- Classification and retrieval of neuronal data using geometry-based approaches
- Image registration, super-resolution and resampling for brain imaging data
- Brain imaging data transformation/synthesis
Zhi Zhou Ph.D. is a software engineer in Microsoft Corporation, a commercial producer of computer software, consumer electronics, personal computers, and related services. This should not pose any conflict for this project, as he will maintain objectivity.
