Enhancing Mitochondrial Image Segmentation with Extended U-Net

Accurate segmentation of mitochondria from electron microscopy data holds crucial significance in unraveling intricate cellular structures and functions. In this paper, we present a comprehensive approach to enhance the semantic segmentation of mitochondria through the integration of an extended U-Net model. Our proposed model introduces an additional layer, encompassing both encoder and decoder components, optimized for capturing intricate mitochondrial features. Moreover, we delve into the impact of employing diverse loss functions, training epochs, and dataset augmentation to assess their effects on segmentation performance. The Mean Intersection over Union (Mean IoU) and Dice Coefficient (DSC) metric serves as our indicator of segmentation quality. Our experimental findings highlight the effectiveness of the extended model and underscore the importance of tailored loss functions and training strategies, culminating in substantial advancements in mitochondria segmentation accuracy.