期刊
NEUROSCIENCE LETTERS
卷 814, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.neulet.2023.137412
关键词
Brain slice alignment; Atlas registration; Deep learning; Geometrical transformation; Feature point recognition
This article introduces DLATA, a deep learning-assisted transformation alignment method that can automatically identify feature points in brain slice images and perform geometric transformations for alignment using a local weighted mean method. It achieves higher efficiency and better results compared to other semi-automated alignment methods.
Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro brain studies. Current semi-automated alignment workflows require labor intensive labeling of feature points on each slice image, which is time-consuming. To speed up the process in large-scale studies, we propose a method called Deep Learning-Assisted Transformation Alignment (DLATA), which uses deep learning to automatically identify feature points in images after training on a few labeled samples. DLATA only requires approximately 10% of the sample size of other semi-automated alignment workflows. Following feature point recognition, local weighted mean method is used as a geometrical transformation to align slice images for registration, achieving better results with about 4 fewer pixels of error than other semi-automated alignment workflows. DLATA can be retrained and successfully applied to the alignment of other biological tissue slices with different stains, including the typically challenging fluorescent stains. Reference codes and trained models for Nissl-stained coronal brain slices of mice can be found at https://github.com/ ALIGNMENT2023/DLATA.
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