Abstract
The complex and highly intertwined morphology of activated platelets within thrombi poses significant challenges for segmentation. In this work, we present a robust dual-network pipeline for cell and organelle segmentation. This multi-network approach enables the detection of fine details near the membrane while simultaneously facilitating long-range smoothing in regions distal to the membrane, drastically improving the performance of the watershed clustering algorithm compared to single-network approaches. We further enhance segmentation performance by collecting neural network predictions along multiple axes, capturing 3D correlations using only 2D neural networks. We segment and analyze hundreds of platelets and report quantitative morphological measurements, showing volumes consistent with hand-segmented results. We apply our segmentation pipeline to the CREMI neuron segmentation challenge data and provide state-of-the-art results.