明场组织学切片中的细胞分割是医学图像分析中的一个关键主题。获得准确的分割使研究人员能够检查细胞形态和临床观察之间的关系。不幸的是，当今已知的大多数分割方法仅限于细胞核，无法分割细胞质。我们提出了一种新的网络架构 Cyto R-CNN，它能够在明场图像中准确分割整个细胞（包括细胞核和细胞质）。我们还提出了一个新的数据集 CytoNuke，其中包含数千个头颈鳞状细胞癌细胞的手动注释。利用该数据集，我们将 Cyto R-CNN 的性能与其他流行的细胞分割算法进行了比较，包括 QuPath 的内置算法、StarDist、Cellpose 和多尺度 Attention Deeplabv3。为了评估分割性能，我们计算了 AP50、AP75，并测量了所有检测到的细胞的 17 个形态和染色相关特征。我们使用 Kolmogorov-Smirnov 测试将这些测量结果与手动分割的黄金标准进行比较。Cyto R-CNN 在全细胞分割中实现了 58.65% 的 AP50 和 11.56% 的 AP75，优于所有其他方法 (QuPath 19.46/0.91% ；StarDist 45.33/2.32%；Cellpose 31.85/5.61%，Deeplabv3 3.97/1.01%）。来自 Cyto R-CNN 的细胞特征显示出与黄金标准的最佳一致性 (D ́= 0.15)，优于 QuPath (D ̅= 0.22)、StarDist (D ̅= 0.25)、Cellpose (D ̅= 0.23) 和 Deeplabv3 (D ¯=0.33)。我们新提出的 Cyto R-CNN 架构在全细胞分割方面优于当前算法，同时提供比任何其他模型更可靠的细胞测量。这可以改善数字病理学工作流程，从而有可能改善诊断。此外，我们发布的数据集可用于将来开发进一步的模型。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm.We present a new network architecture Cyto R-CNN that is able to accurately segment whole cells (with both the nucleus and the cytoplasm) in bright-field images. We also present a new dataset CytoNuke, consisting of multiple thousand manual annotations of head and neck squamous cell carcinoma cells. Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist, Cellpose and a multi-scale Attention Deeplabv3+. To evaluate segmentation performance, we calculated AP50, AP75 and measured 17 morphological and staining-related features for all detected cells. We compared these measurements to the gold standard of manual segmentation using the Kolmogorov-Smirnov test.Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in whole-cell segmentation, outperforming all other methods (QuPath 19.46/0.91%; StarDist 45.33/2.32%; Cellpose 31.85/5.61%, Deeplabv3+ 3.97/1.01%). Cell features derived from Cyto R-CNN showed the best agreement to the gold standard (D¯=0.15) outperforming QuPath (D¯=0.22), StarDist (D¯=0.25), Cellpose (D¯=0.23) and Deeplabv3+ (D¯=0.33).Our newly proposed Cyto R-CNN architecture outperforms current algorithms in whole-cell segmentation while providing more reliable cell measurements than any other model. This could improve digital pathology workflows, potentially leading to improved diagnosis. Moreover, our published dataset can be used to develop further models in the future.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.