Deep Learning-Driven Uncertainty Estimation for Brain Tissue Segmentation by Integrating Expectation Maximization Algorithms

Brain tissue segmentation performs a vital function in neuroimaging analysis, support, and monitoring of neurological diseases such as Alzheimer's disease, Parkinson's disease, and brain tumors. However, due to the complexity of tissue boundaries and noise in the images, accurately segmenting brain tissues from MRI scans is challenging. An advanced approach combines Deeplabv3+, a deep learning architecture for semantic segmentation, with Uncertainty Estimation and the Expectation Maximization (EM) algorithm to obtain increased segmentation accuracy. The proposed framework has two parts. The first part, encoder-decoder-based Deeplabv3+ architecture is implemented with Monte Carlo Dropout (MCD) in which the different features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique passed into the output layer of segmentation. The second part proposes that the EM algorithm be used to refine the segmented result iteratively by maximizing the likelihood of the recorded data using the Gaussian Mixture Model(GMM). This integration of uncertainty-driven segmentation with EM-based refinement yields an improvement of 2.12% in the Dice score compared to using Deeplabv3+ alone. Combining deep learning models with probabilistic algorithms highlights the potential for acquiring more dependable and precise brain tissue segmentation. Overall, this research paper not only enhances the accuracy of segmentation in complex medical imaging scenarios but also contributes to improving clinical decision-making and analysis.