AUTHOR=Liu Xin , Li Anan , Luo Yue , Bao Shengda , Jiang Tao , Li Xiangning , Yuan Jing , Feng Zhao TITLE=An interactive image segmentation method for the anatomical structures of the main olfactory bulb with micro-level resolution JOURNAL=Frontiers in Neuroinformatics VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2023.1276891 DOI=10.3389/fninf.2023.1276891 ISSN=1662-5196 ABSTRACT=

The main olfactory bulb is the key element of the olfactory pathway of rodents. To precisely dissect the neural pathway in the main olfactory bulb (MOB), it is necessary to construct the three-dimensional morphologies of the anatomical structures within it with micro-level resolution. However, the construction remains challenging due to the complicated shape of the anatomical structures in the main olfactory bulb and the high resolution of micro-optical images. To address these issues, we propose an interactive volume image segmentation method with micro-level resolution in the horizontal and axial direction. Firstly, we obtain the initial location of the anatomical structures by manual annotation and design a patch-based neural network to learn the complex texture feature of the anatomical structures. Then we randomly sample some patches to predict by the trained network and perform an annotation reconstruction based on intensity calculation to get the final location results of the anatomical structures. Our experiments were conducted using Nissl-stained brain images acquired by the Micro-optical sectioning tomography (MOST) system. Our method achieved a mean dice similarity coefficient (DSC) of 81.8% and obtain the best segmentation performance. At the same time, the experiment shows the three-dimensional morphology reconstruction results of the anatomical structures in the main olfactory bulb are smooth and consistent with their natural shapes, which addresses the possibility of constructing three-dimensional morphologies of the anatomical structures in the whole brain.