An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain
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Abstract (arXiv)
We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of data in the genome-wide whole-brain dataset, and to process images that have varying intensity profiles and expression patterns with minimal human bias. To reduce the number of labelled images required for training, we develop a semi-supervised segmentation model. We further develop an iterative algorithm to register images to a standard space, enabling comparative analysis between genes and concurrent visualization with other datasets, thereby facilitating a more holistic understanding of primate brain structure and function.
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References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
- 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
- 2021HyperMorph: Amortized Hyperparameter Learning for Image Registrationcited by 85
- 2018Semi-Supervised Semantic Image Segmentation with Self-correcting Networkscited by 37
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