# Automatic Segmentation of Histological Images of Mouse Brains
## 1 Introduction  
A major goal of neuroscience is to understand the complex functions of the brain. One approach is to study brain anatomy. Anatomical phenotypes involve observable and measurable structural traits influenced by both genetics and the environment. These traits include macroscopic characteristics like brain region size and shape, as well as microscopic features such as neuron organization and connectivity. The integration of anatomy and function enhances our understanding of how the brain develops. The study of neuroanatomical phenotypes offers valuable insights into how variations in brain structure relate to differences in development, function and behavior. This is particularly suited in animal models where the brain can be easily accessed. Furthermore, the mouse model provides powerful tools to establish connections between phenotypes and genotypes, especially since the environment can be controlled and manipulating the mouse genome is relatively straightforward. This enables researchers to study pathogenicity at the molecular, cellular, physiological, and behavioral levels [1]. Historically, to tap into the potential of mouse models, global scientific communities recognized the need for a cohesive effort to explore gene function on a vast scale. The International Mouse Consortium was born out of this recognition [2], aiming to undertake a systematic study ofthe mouse genome. This endeavor laid the groundwork for the subsequent formation of the International Mouse Phenotyping Consortium (IMPC) [3], which expanded upon the initial efforts by developing large-scale phenotyping pipelines such as the DMDD program. These pipelines facilitated an exhaustive examination of gene-phenotype relationships. The NeuroGeMM laboratory of the University of Burgundy (France) developed a high throughput precision histology pipeline to characterize neuroanatomical phenotypes (NAPs) from mice generated by the IMPC. NAPs are used as endophenotypes of neurological disorders to detect specific genes influencing brain susceptibility to developmental disorders. A first list of 198 genes affecting brain morphogenesis through a high-throughput screen of 1500 knock out lines was published in 2019 (mouse lines where a specific gene has been inactivated) [4]. And, more recently, the characterization of 20 genes out of 30 autism candidate genes at the 16p11.2 locus enabled us to highlight, for the first time, _MVP_ as a key morphogene and candidate [5]. The evaluation of anatomical abnormalities was typically performed on high resolution histological images (24,000 pixels \(\times\) 14,000 pixel sizes at \(\sim\)0.45 micrometer/pixel resolution), and this task was carried out using manual annotation of the different regions of the mouse brain with manual contouring or semi-automatic software assisted methods. Manual segmentation of medical images involves a human expert manually delineating structures or regions of interest on the images. Experience and precision are required to accurately delineate the boundaries of anatomical structures or lesions using computational software (e.g., Fiji 1.54, ITKsnap 4.0.2, 3D slicer 5.6, etc.). This approach is an absolute prerequisite for evaluating automated or semi-automated methods. It is considered as the gold standard in cases requiring high precision and accurate delineation, such as radiation therapy, disease diagnosis or image-guided interventions. Semi-automatic segmentation of medical images has served as a valuable intermediate step between manual and automatic approaches. It has allowed for user interaction and guidance to refine segmentation results while reducing the manual effort required. However, the field is continuously advancing towards fully automatic segmentation methods. The goal is to minimize user involvement and rely on advanced computational algorithms, such as deep learning, for accurate and efficient segmentation of medical images. New advances continuously enable the development of systems designed to assist in the analysis of histological images.