However, in these schemes, feature acquisition is still heavily dependent on interactive manual methods and experience moreover, these approaches are typically expensive and are inefficient for addressing large-scale image datasets. performed three-dimensional (3D) registration of cellular optical images to a standard brain space using a number of manually acquired nuclei regional features (Ni et al. ![]() registered microscopy images to a reference brain atlas by interactively selecting feature points (Fürth et al. ![]() registered two-dimensional (2D) micro-optical images to an MRI image by manually locating the feature points (Ohnishi et al. ![]() However, for mesoscopic optical images, different types of neurons exhibit different image characteristics in different brain regions and diverse labeling strategies are applied therefore, feature-based registration methods are widely used (Fürth et al. In particular, a series of representative and effective registration algorithms have been developed for macro MRI images (Klein et al. 2003) in that has been widely studied in brain science (Niedworok et al. Image registration is a fundamental image processing problem (Zitova et al. Additionally, the advent of the terabyte-scale (TB-scale) mouse brain dataset (Landhuis 2017) has promoted the need for a stable and reliable high-throughput automatic registration method suitable for these large datasets. 2012) have resulted in brain images becoming increasingly complicated at the mesoscopic level. Moreover, the rapid development of neural circuit labeling methods and whole-brain imaging technologies (Gong et al. However, mainly due to the differences in individual animals and the conditions of specific experimental settings, the correspondence of brain structures with an atlas is highly dependent on personal experience and proficiency (Fürth et al. 2018 Osten and Margrie 2013) with the help of a brain stereotactic reference atlas. In biological research, neuroscientists often manually delineate the brain regions and nuclei in which neurons are located (Fürth et al. DeepMapi achieves fully automatic registration of mesoscopic micro-optical images, even macroscopic MRI datasets, in minutes, with an accuracy comparable to those of manual annotations by anatomists.ĭeconstructing fine neural structures at the cellular level is critical in understanding the connections and collaborations of brain networks (Economo et al. By comparing DeepMapi with other registration methods, we demonstrate its superiority over a set of ground truth images, including both optical and MRI images. We use a self-feedback strategy to address the problem of imbalanced training sets (sampling at a fixed step size in nonuniform brains of structures and deformations) and use a dual-hierarchical network to capture the large and small deformations. Here, we propose a deep learning-based registration method called DeepMapi to predict a deformation field used to register mesoscopic optical images to an atlas. As a result, it is difficult to achieve high-throughput automatic registration without manual intervention. However, different brain images are normally not naturally aligned even when they are imaged with the same setup, let alone under the differing resolutions and dataset sizes used in mesoscopic imaging. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. ![]() The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures.
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