  Segmenting 3D cell nuclei from microscopy image volumes is critical for
biological and clinical analysis, enabling the study of cellular expression
patterns and cell lineages. However, current datasets for neuronal nuclei
usually contain volumes smaller than $10^{\text{-}3}\ mm^3$ with fewer than 500
instances per volume, unable to reveal the complexity in large brain regions
and restrict the investigation of neuronal structures. In this paper, we have
pushed the task forward to the sub-cubic millimeter scale and curated the NucMM
dataset with two fully annotated volumes: one $0.1\ mm^3$ electron microscopy
(EM) volume containing nearly the entire zebrafish brain with around 170,000
nuclei; and one $0.25\ mm^3$ micro-CT (uCT) volume containing part of a mouse
visual cortex with about 7,000 nuclei. With two imaging modalities and
significantly increased volume size and instance numbers, we discover a great
diversity of neuronal nuclei in appearance and density, introducing new
challenges to the field. We also perform a statistical analysis to illustrate
those challenges quantitatively. To tackle the challenges, we propose a novel
hybrid-representation learning model that combines the merits of foreground
mask, contour map, and signed distance transform to produce high-quality 3D
masks. The benchmark comparisons on the NucMM dataset show that our proposed
method significantly outperforms state-of-the-art nuclei segmentation
approaches. Code and data are available at
https://connectomics-bazaar.github.io/proj/nucMM/index.html.

Computer Vision and Pattern Recognition