DLATA : Deep Learning-Assisted transformation alignment of 2D brain slice histology

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number137412
Journal / PublicationNeuroscience Letters
Volume814
Online published9 Aug 2023
Publication statusPublished - 25 Sept 2023

Abstract

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. © 2023 Elsevier B.V.

Research Area(s)

  • Atlas registration, Brain slice alignment, Deep learning, Feature point recognition, Geometrical transformation

Citation Format(s)

DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology. / Luo, Moxuan; Liu, Qingqing; Wang, Liping et al.
In: Neuroscience Letters, Vol. 814, 137412, 25.09.2023.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review