Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge

Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces toward predefined anatomical targets, employing an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. Additionally, we propose an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among the segmented structures. Each deformable surface corresponds to a specific structure. To validate our approach, we conducted experiments on a dataset of real brain images (IBSR) and compared the results with several state-of-the-art methods. The obtained results were promising, demonstrating the effectiveness and superiority of our developed method.

Automated summary

This paper introduces a novel and robust approach for simultaneously segmenting multiple brain structures in MRI images, using an efficient multi-object generalized fast marching method (MOGFMM) for simultaneous object detection. It also proposes an effective evolution function that integrates prior knowledge from anatomical and probabilistic atlases, as well as spatial relationships among segmented structures. Experimental results on a dataset of real brain images (IBSR) demonstrate the effectiveness and superiority of the developed method.