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Abstract:
Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, we explore the efficacy of implicit neural representations (INRs) in improving the performance of brain image registration in magnetic resonance imaging. In this setting, INRs serve as a continuous and coordinate based approximation of the deformation field obtained through a multi-layer perceptron. Previous research has demonstrated that sinusoidal representation networks (SIRENs) surpass ReLU models in performance. In this study, we first broaden the range of activation functions to further investigate the registration performance of implicit networks equipped with activation functions that exhibit diverse oscillatory properties. Specifically, in addition to the SIRENs and ReLU, we evaluate activation functions based on snake, sine+, chirp and Morlet wavelet functions. Second, we conduct experiments to relate the hyper-parameters of the models to registration performance. Third, we propose and assess various techniques, including cycle consistency loss, ensembles and cascades of implicit networks, as well as a combined image fusion and registration objective, to enhance the performance of implicit registration networks beyond the standard approach. The investigated implicit methods are compared to the VoxelMorph convolutional neural network and to the symmetric image normalization (SyN) registration algorithm from the Advanced Normalization Tools (ANTs). Our findings not only highlight the remarkable capabilities of implicit networks in addressing pairwise image registration challenges, but also showcase their potential as a powerful and versatile off-the-shelf tool in the fields of neuroscience and radiology.

Abstract:
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.

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