Perth Machine Learning Group, Perth, WA, 6000, Australia.
School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia.
Sci Rep. 2021 Aug 19;11(1):16917. doi: 10.1038/s41598-021-96067-3.
Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network ("DCNet"), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell class. The single centre point annotation for DCNet lowered burden for experts to generate ground truth data by 77.1% compared to bounding box labeling. Yet centre point annotation still enabled high accuracy when training DCNet on a multi-class algorithm on whole cell features, matching human experts in all 5 object classes in average precision and outperforming humans in consistency. The efficacy and efficiency of the DCNet end-to-end system represents a significant progress toward an open source, fully computationally approach to differential cell count based diagnosis that can be adapted to any pathology need.
当将计算机视觉算法应用于病理学时，细胞差异计数是一项具有挑战性的任务。现有的细胞识别训练方法需要多类别分割和/或边界框注释的高可用性，并且在对象紧密聚集时性能会受到影响。我们提出了差分计数网络（“DCNet”），这是一种高效的注释模式，利用关键点检测在明场图像中定位细胞（非细胞核）的中心点及其细胞类别。与边界框标记相比，DCNet 的单个中心点注释将专家生成真实数据的负担降低了 77.1%。然而，当 DCNet 基于全细胞特征在多类别算法上进行训练时，中心点注释仍然能够实现高精度，在平均精度方面与人类专家匹配，在一致性方面优于人类专家。DCNet 端到端系统的有效性和效率代表了在基于差异细胞计数的开源、完全计算方法诊断方面的重大进展，该方法可以适应任何病理学需求。
