Three Dimensional Synthetic Non-Ellipsoidal Nuclei Volume Generation Using Bézier Curves.

Automated segmentation of cell nuclei is used to analyze individual cells to determine the number of nuclei in a 3D volume. Deep learning approaches that segment nuclei require large amounts of annotated (ground truth) microscopy volumes for training. In many cases acquiring large amounts of annotated volumes may not be possible and data augmentation methods must be used. One approach has been the use of synthetic volumes for training. Alternate methods employ spherical and ellipsoidal nuclear models for synthetic ground truth generation, resulting in segmentation that does not accurately match nuclei morphology. In this paper, we present a technique to generate synthetic non-ellipsoidal nuclei microscopy images using Bezier Curves. We test our approach by training a modified 3D U-Net. Our results indicates that our synthetic non-ellipsoidal nuclei approach achieves improved segmentation on volumes with irregularly shaped nuclei.