We address the problem of cell segmentation in confocal microscopy membrane volumes of the ascidian Ciona used in the study of morphogenesis. The primary challenges are non-uniform and patchy membrane staining and faint spurious boundaries from other organelles (e.g. nuclei). Traditional segmentation methods incorrectly attach to faint boundaries producing spurious edges. To address this problem, we propose a linear optimization framework for the joint correction of multiple over-segmentations obtained from different methods. The main idea motivating this approach is that multiple over-segmentations, resulting from a pool of methods with various parameters, are likely to agree on the correct segment boundaries, while spurious boundaries are methodor parameter-dependent. The challenge is to make an optimized decision on selecting the correct boundaries while discarding the spurious ones. The proposed unsupervised method achieves better performance than state of the art methods for cell segmentation from membrane images.