Topological 3D reconstruction of multiple anatomical structures from volumetric medical data

In the medical field, usually, practitioners mainly base their analysis on 2D slices produced from MRI or CT-scans that correspond to restricted views of a pathology. To facilitate the work of doctors, increase diagnostic accuracy and cross-reference multimodal data, a 3D reconstruction is required. However, most of the time, reconstruction methods fail at visualizing complex and noisy data made up of several tissues. Indeed, these methods often build each tissue independently so that the consistency of the global model is not ensured: overlaps may appear between segments whereas some disjointed volumes exhibit empty spaces. This paper presents a complete topologically consistent reconstruction system from 3D medical acquisitions such as MRI or CT-scans. Compared to other methods, our system offers a single volumetric representation of an organ corresponding to a 3D space partition, where a semantic label is associated to each volume to identify the represented tissue and adjacency between volumes is explicitly and precisely defined. This partition is controlled and free from topological and geometric defects usually found in other 3D reconstruction approaches. Experimental studies were conducted on MRI datasets of brains resulting in consistent reconstructions. An application of the model for calculating the distribution of physiological data in brain tissue is also shown.