Partnerzy

Streszczenie:
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 seg-
mentation 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 algo-
rithm to register images to a standard space, enabling com-
parative analysis between genes and concurrent visualization
with other datasets, thereby facilitating a more holistic under-
standing of primate brain structure and function.

Słowa kluczowe:
contrastive learning, gene atlas, segmen-tation, semi-supervised learning, registration

Streszczenie:
We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of brain images with similar anatomical structures, but where one image contains additional features or artifacts that are not present in the other image. To demonstrate its effectiveness, we use 2D microscopy in situ hybridization gene expression images of the marmoset brain. Accurately quantifying gene expression requires image registration to a brain template, which is difficult due to the diversity of patterns causing variations in visible anatomical brain structures. Our approach uses implicit networks in combination with an image exclusion loss to jointly perform the registration and decompose the image into a support and residual image. The support image aligns well with the template, while the residual image captures individual image characteristics that diverge from the template. In experiments, our method provided excellent results and outperformed other registration techniques.

Słowa kluczowe:
brain, deep learning, gene expression, implicit neural representations, registration