Toward few pixel annotations for 3D segmentation of material from electron tomography
Abstract
Segmentation is a notorious tedious task, especially for 3D volume of material obtained via electron tomography. In this paper, we propose a new method for the segmentation of such data with only few partially labeled slices extracted from the volume. This method handles very restricted training data, and particularly less than a slice of the volume. Moreover, unlabeled data also contributes to the segmentation. To achieve this, a combination of self-supervised and contrastive learning methods are used on top of any 2D segmentation backbone. This method has been evaluated on three real electron tomography volumes.
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