Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2985-2988. doi: 10.1109/EMBC46164.2021.9630914.
Cell individualization has a vital role in digital pathology image analysis. Deep Learning is considered as an efficient tool for instance segmentation tasks, including cell individualization. However, the precision of the Deep Learning model relies on massive unbiased dataset and manual pixel-level annotations, which is labor intensive. Moreover, most applications of Deep Learning have been developed for processing oncological data. To overcome these challenges, i) we established a pipeline to synthesize pixel-level labels with only point annotations provided; ii) we tested an ensemble Deep Learning algorithm to perform cell individualization on neurological data. Results suggest that the proposed method successfully segments neuronal cells in both object-level and pixel-level, with an average detection accuracy of 0.93.
细胞个体化在数字病理学图像分析中起着至关重要的作用。深度学习被认为是实例分割任务（包括细胞个体化）的有效工具。然而，深度学习模型的精度依赖于大量无偏数据集和手动像素级注释，这是劳动密集型的。此外，深度学习的大多数应用都是为处理肿瘤学数据而开发的。为了克服这些挑战，我们：i）建立了一个仅使用点注释生成像素级标签的流水线；ii）测试了一个集成深度学习算法，以便在神经学数据上执行细胞个体化。结果表明，该方法成功地对神经元细胞进行了对象级和像素级分割，平均检测准确率为 0.93。
