3D Flattering Amplified Neural Network-Based Segmentation of Amygdala and Hippocampus

Recent emergence in deep learning resulted in significant improvement in the segmentation accuracy of sub cortical brain structures like hippocampus and amygdala. The traditional methods of segmentation cannot produce an ideal segmentation result that exhibits issues like redundant computations, inconsistencies, coefficient variations and motion artifacts. Therefore, in this paper, an improved 3D Flatteringly Amplified Neural Network model for biomedical imaging is efficiently proposed, which can make full use of the 3D spatial information of MRI image itself to overcome the inconsistency of segmented images along with equalizing the coefficient variation of tiny region of brain image segmentation. Also while equalizing the coefficient, certain significant minute details are lost due to motion artifacts hence, the robust Amyg-Hippo Seg algorithm has been introducing that extracts the features through deep learning, and achieve high-precision segmentation, it reduced the computational complexity without neglecting minute features. In addition, the Daytona dropout function provides uncertainty information and reduces over-fitting problems. The outcome of the proposed work efficiently segments the most significant regions of hippocampus and amygdala with 97.4% accuracy.