Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects. Although many automated methods for these tasks exist, they often struggle for images with low signal-to-noise ratios and/or dense packing of nuclei. It was recently shown for 2D microscopy images that these issues can be alleviated by training a neural network to directly predict a suitable shape representation (star-convex polygon) for cell nuclei. In this paper, we adopt and extend this approach to 3D volumes by using star-convex polyhedra to represent cell nuclei and similar shapes. To that end, we overcome the challenges of 1) finding parameter-efficient star-convex polyhedra representations that can faithfully describe cell nuclei shapes, 2) adapting to anisotropic voxel sizes often found in fluorescence microscopy datasets, and 3) efficiently computing intersections between pairs of star-convex polyhedra (required for non-maximum suppression). Although our approach is quite general, since star-convex polyhedra subsume common shapes like bounding boxes and spheres as special cases, our focus is on accurate detection and segmentation of cell nuclei. That that end, we demonstrate on two challenging datasets that our approach (StarDist-3D) leads to superior results when compared to classical and deep-learning based methods.

在三维荧光显微镜数据集中，通过使用星形凸多面体来表示细胞核和类似形状，本文采用并扩展了2D显微镜图像的方法来进行准确的检测和分割。与传统和基于深度学习的方法相比，我们的方法（StarDist-3D）在两个具有挑战性的数据集上表现出更好的结果。

用于微观镜像中3D物体检测和分割的星形凸多面体