WeakMitoSAM: competitive prompt aggregation for point-supervised mitochondria segmentation in electron microscopy images

Mitochondrial morphology is a critical indicator of cellular metabolic status and disease pathogenesis, requiring high-resolution visualization and precise segmentation in electron microscopy (EM) images. While fully supervised deep learning models have achieved significant progress, their reliance on dense pixel-wise annotations presents a major bottleneck due to the labor-intensive labeling process and expert variability near the optical diffraction limit. Existing weakly supervised methods, primarily designed for densely packed instances, often fail to generalize to the sparse distribution of mitochondria in EM data. In this paper, we propose WeakMitoSAM, a novel weakly supervised framework for high-precision mitochondria segmentation using sparse point annotations. Our approach introduces the competitive aggregation of multiple prompts strategy, which employs a Bias-augmented Softmax mechanism to reconcile semantic ambiguities and suppress background noise, effectively converting sparse priors into high-fidelity pseudo-labels. Subsequently, segment anything model is specialized for mitochondrial ultrastructures via low-rank adaptation, ensuring parameter-efficient domain adaptation. Experimental results across four public EM datasets demonstrate that WeakMitoSAM achieves state-of-the-art performance in point-supervised scenarios and even outperforms several fully supervised benchmarks, providing an efficient and robust solution for large-scale mitochondrial morphofunctional analysis.