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To accurately explore the anatomical organization of neural circuits in the brain, it is crucial to map the experimental brain data onto a standardized system of coordinates. Studying 2D histological mouse brain slices remains the standard procedure in many laboratories. Mapping these 2D brain slices is challenging; due to deformations, artifacts, and tilted angles introduced during the standard preparation and slicing process. In addition, analysis of experimental mouse brain slices can be highly dependent on the level of expertise of the human operator. Here we propose a computational tool for Accurate Mouse Brain Image Analysis (AMBIA), to map 2D mouse brain slices on the 3D brain model with minimal human intervention. AMBIA has a modular design that comprises a localization module and a registration module. The localization module is a deep learning-based pipeline that localizes a single 2D slice in the 3D Allen Brain Atlas and generates a corresponding atlas plane. The registration module is built upon the Ardent python package that performs deformable 2D registration between the brain slice to its corresponding atlas. By comparing AMBIA's performance in localization and registration to human ratings, we demonstrate that it performs at a human expert level. AMBIA provides an intuitive and highly efficient way for accurate registration of experimental 2D mouse brain images to 3D digital mouse brain atlas. Our tool provides a graphical user interface and it is designed to be used by researchers with minimal programming knowledge.

Purpose The aim of this work was to introduce a Computer Aided Design (CAD)tool that enables the design of large skull defects (> 100cm2) implants. Functional and aesthetically correct custom implants are extremely important for patients with large cranial defects. For these cases pre-operative fabrication of implants is recommended to avoid problems of donor site morbidity, sufficiencyof donor material and quality. Finally, crafting the correct shape is a non-trivial task increasingly complicated by defect size. Methods We present a Computer Aided Design (CAD) tool to design such implants for the neurocranium. A combination of Geometric Morphometrics (GM) and Radial Basis Functions (RBF), namely Thin Plate Splines (TPS), allows semiautomatic implant generation. The method uses symmetry and the best fitting shape to estimate missing data directly within the radiologic volume data. In addition, this approach delivers correct implant fitting via a boundary fitting approach. Results This method generates a smooth implant surface, free of sharp edges that follows the main contours of the boundary, enabling accurate implant placementin the defect site intraoperatively. The present approach is evaluated and compared to existing methods. A mean error of 89.29% (72.64% - 100%)missing landmarks with an error less or equal to 1 mm was obtained. Conclusion In conclusion, the results show that our Computer Aided Design (CAD)tool can generate patient specific implants with high accuracy., Funding agencies: EU [MRTN-CT-2005-019564], EVAN