论文标题
MR-NOM：通过故意过度分割和合并，NISSL染色的组织学切片中神经元细胞的多尺度分辨率
MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging
论文作者
论文摘要
在比较神经解剖学中，脑细胞结构的表征对于更好地理解大脑结构和功能至关重要，因为它有助于提炼有关不同人群的发展，进化和独特特征的信息。单个脑细胞的自动分割是主要的先决条件，但仍然具有挑战性。开发了一种新方法（MR-NOM），用于在大脑的NISSL染色的组织学图像中分割细胞的实例分割。 MR-NOM利用了一种多尺度方法，将细胞故意将细胞分为超像素，然后通过基于形状，结构和强度特征的分类器将它们合并。该方法已在大脑皮层的图像上进行了测试，证明成功地处理了部分触摸或重叠的不同特征的细胞，比两种最先进的方法表现出更好的性能。
In comparative neuroanatomy, the characterization of brain cytoarchitecture is critical to a better understanding of brain structure and function, as it helps to distill information on the development, evolution, and distinctive features of different populations. The automatic segmentation of individual brain cells is a primary prerequisite and yet remains challenging. A new method (MR-NOM) was developed for the instance segmentation of cells in Nissl-stained histological images of the brain. MR-NOM exploits a multi-scale approach to deliberately over-segment the cells into superpixels and subsequently merge them via a classifier based on shape, structure, and intensity features. The method was tested on images of the cerebral cortex, proving successful in dealing with cells of varying characteristics that partially touch or overlap, showing better performance than two state-of-the-art methods.
