Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a state-of-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases.
翻译:最近的研究表明,在医学图像分析中,特别是在细胞切片分析中,深层学习是医学图像分析的优越性,特别是在细胞切片中,这是许多生物研究的一个基本步骤。然而,神经网络的出色表现需要大规模、无偏倚的数据集和说明培训,这是一个劳动密集型和专门知识需求型的大规模、无偏倚的数据集和说明。本文件展示了一个端对端框架,用于仅使用点说明自动检测和将NeuNeu沾染神经细胞在生理图像中进行分解。与带有点注的传统核分解不同的是,我们提议使用点注解和二元分解来合成像素水平的注释。合成面罩作为地面真理,用于培训神经网络,这是一个类似U-Net的架构,具有先进的网络,作为编码器。 校验结果表明,我们的模型优于最近的其他方法。 此外,我们研究了多个后处理计划,并提出了一个原始战略,将概率图转换成分解的例子,使用最终的侵蚀和动态重建。这种方法很容易配置和超越其他古典后处理技术。这项工作旨在开发一个更牢固和高效的神经病前的模型研究框架,在生物学前的模型研究中可以具体地用来分析神经病学学学上。