Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells.

翻译：深度学习已被证明在医学图像分析中比其它方法更为有效，包括看似简单但极具挑战性的单细胞分割任务——这是许多生物学研究的关键步骤。比较神经解剖学研究正是典型范例，其中神经元细胞的实例分割对细胞构筑特征描述至关重要。本文提出一种端到端框架，可自动分割尼氏染色脑组织学图像中的单个神经元细胞，从而为研究脑细胞构筑变化提供可靠的形态学与结构分析基础。该框架采用U-Net-like架构，以EfficientNet为编码器并配备两个解码分支，通过回归四张彩色梯度图，将像素分类为接触细胞间轮廓、细胞体或背景。解码分支间通过注意力门控连接以共享相关特征，其输出经融合后获得细胞的实例分割结果。该方法在脑皮层与小脑图像上进行了测试，在细胞实例分割任务中优于近期其他基于深度学习方法。