ArticleFrontiers in neuroinformatics2021
Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 11 citations in OpenAlex.
- An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration.Nature methods · 2023Article
- Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space.Neuroinformatics · 2023Article
- Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq andFrontiers in neuroanatomy · 2023Article
- giRAff: an automated atlas segmentation tool adapted to single histological slices.Frontiers in neuroscience · 2023Article
- A method to estimate the cellular composition of the mouse brain from heterogeneous datasets.PLoS computational biology · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task encumbered by the diversity of staining techniques employed, and by deformations and artifacts in the soft tissue. Recently, deep learning models have garnered particular interest as a viable alternative to traditional intensity-based algorithms for image registration. In this work, we propose a supervised learning model for general multimodal 2D registration tasks, trained with a perceptual similarity loss on a dataset labeled by a human expert and augmented by synthetic local deformations. We demonstrate the results of our approach on the Allen Mouse Brain Atlas (AMBA), comprising whole brain Nissl and gene expression stains. We show that our framework and design of the loss function result in accurate and smooth predictions. Our model is able to generalize to unseen gene expressions and coronal sections, outperforming traditional intensity-based approaches in aligning complex brain structures.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.