Deep Learning Approach for Layer-Specific Segmentation of the Olfactory Bulb in X-ray Phase-Contrast Tomography

This paper addresses neural network segmentation of a human olfactory bulb sample on X-ray phase-contrast tomographic reconstruction. The olfactory bulb plays a key role in the primary processing of olfactory information. It consists of several nested cell layers, the morphometric analysis of which has important diagnostic value. However, manual segmentation of the reconstructed volume is labor-intensive and requires high qualifications, which makes the development of automated segmentation methods crucial. X-ray phase-contrast tomography provides a high-resolution reconstruction of the olfactory bulb morphological structure. The resulting reconstructions are characterized by excessive morphological details and reconstruction artifacts. These features, combined with limited data volume, visual similarity of neighboring slices, and sparse ground truth, hinder the application of standard neural network-based segmentation approaches. This paper examines the characteristics of the data under consideration and suggests a training pipeline for a convolutional neural network, including inter-slice smoothing at the data preprocessing stage, alternative strategies for splitting the data into subsets, a set of augmentations, and training on sparse sampling. The proposed adaptations achieved a Dice score (micro) value of 0.93 on the test subset. An ablation study demonstrated that each of the above-mentioned modifications independently improves segmentation quality. The presented training pipeline can be applied to the segmentation of morphological structures on tomographic images in biomedical tasks with a limited dataset and non-standard ground truth.