Sequential semi-supervised segmentation for serial electron microscopy image with small number of labels.

BACKGROUND: Segmentation of electron microscopic continuous section images by deep learning has attracted attention as a technique to reduce the cost of annotation for researchers attempting to make observations using 3D reconstruction methods. However, when the observed samples are rare, or scanning circumstances are unstable, pursuing generalization performance for newly obtained samples is not appropriate. NEW METHODS: We assume a transductive setting that predicts all labels in a dataset from only partially obtained labels while avoiding the pursuit of generalization performance for unknown data. Then, we propose sequential semi-supervised segmentation (4S), which semi-automatically extracts neural regions from electron microscopy image stacks. This method focuses on the fact that adjacent images have a strong correlation in serial images. Our 4S repeats training, inference, and pseudo-labeling using a minimal number of teacher labels and performs segmentation on all slices. RESULT: Our experiments using two types of serial section images showed effectiveness in terms of both quality and quantity. In addition, we experimentally clarified the effect of the number and position of teacher labels on performance. COMPARISON WITH EXISTING METHODS: Compared with supervised learning when a small number of labeled data was obtained, the performance of the proposed method was shown to be superior. CONCLUSION: Our 4S leverages a limited number of labeled data and a large amount of unlabeled data to extract neural regions from serial image stacks in a transductive setting. We plan to develop this method as a core module of a general-purpose annotation tool in our future work.

Putative Neural Network Within an Olfactory Sensory Unit for Nestmate and Non-nestmate Discrimination in the Japanese Carpenter Ant: The Ultra-structures and Mathematical Simulation.

Ants are known to use a colony-specific blend of cuticular hydrocarbons (CHCs) as a pheromone to discriminate between nestmates and non-nestmates and the CHCs were sensed in the basiconic type of antennal sensilla (S. basiconica). To investigate the functional design of this type of antennal sensilla, we observed the ultra-structures at 2D and 3D in the Japanese carpenter ant, Camponotus japonicus, using a serial block-face scanning electron microscope (SBF-SEM), and conventional and high-voltage transmission electron microscopes. Based on the serial images of 352 cross sections of SBF-SEM, we reconstructed a 3D model of the sensillum revealing that each S. basiconica houses > 100 unbranched dendritic processes, which extend from the same number of olfactory receptor neurons (ORNs). The dendritic processes had characteristic beaded-structures and formed a twisted bundle within the sensillum. At the "beads," the cell membranes of the processes were closely adjacent in the interdigitated profiles, suggesting functional interactions via gap junctions (GJs). Immunohistochemistry with anti-innexin (invertebrate GJ protein) antisera revealed positive labeling in the antennae of C. japonicus. Innexin 3, one of the five antennal innexin subtypes, was detected as a dotted signal within the S. basiconica as a sensory organ for nestmate recognition. These morphological results suggest that ORNs form an electrical network via GJs between dendritic processes. We were unable to functionally certify the electric connections in an olfactory sensory unit comprising such multiple ORNs; however, with the aid of simulation of a mathematical model, we examined the putative function of this novel chemosensory information network, which possibly contributes to the distinct discrimination of colony-specific blends of CHCs or other odor detection.