Utilizing Reverse Attention for Enhanced Mitochondria Segmentation in Microscopic Images

Mitochondrial segmentation in microscopic images is a complex task hindered by variations in mitochondrial size, color, texture, and their often ambiguous borders with surrounding tissue. To overcome these obstacles, we adapted Parallel Reverse Attention Network (Pranet), originally developed for precise polyp detection, to the domain of mitochondrial segmentation. Pranet employs Parallel Partial Decoder (PPD) to integrate multi-level features, producing a high-level context map that guides subsequent processing. The model further enhances segmentation precision through a Reverse Attention (RA) module, which adeptly identifies object boundaries by iteratively refining region-boundary interactions. Our approach was thoroughly evaluated on the EPFL dataset, yielding substantial improvements in segmentation accuracy. Additionally, Pranet demonstrated robust generalization and real-time performance, underscoring its suitability for comprehensive microscopic image analysis.