Axon Tracing and Centerline Detection using Topologically-Aware 3D U-Nets.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 238-242, 2022 07.
Article
em En
| MEDLINE
| ID: mdl-36085649
ABSTRACT
As advances in microscopy imaging provide an ever clearer window into the human brain, accurate reconstruction of neural connectivity can yield valuable insight into the relationship between brain structure and function. However, human manual tracing is a slow and laborious task, and requires domain expertise. Automated methods are thus needed to enable rapid and accurate analysis at scale. In this paper, we explored deep neural networks for dense axon tracing and incorporated axon topological information into the loss function with a goal to improve the performance on both voxel-based segmentation and axon centerline detection. We evaluated three approaches using a modified 3D U-Net architecture trained on a mouse brain dataset imaged with light sheet microscopy and achieved a 10% increase in axon tracing accuracy over previous methods. Furthermore, the addition of centerline awareness in the loss function outperformed the baseline approach across all metrics, including a boost in Rand Index by 8%.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Imageamento Tridimensional
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article