Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Institute of Pathology, Aalborg University Hospital, Aalborg, Denmark.
Cytometry A. 2019 Oct;95(10):1066-1074. doi: 10.1002/cyto.a.23885. Epub 2019 Sep 6.
Bone marrow cellularity is an important measure in diagnostic hematopathology. Currently, the gold standard for bone marrow cellularity estimation is manual inspection of hematoxylin and eosin stained whole slide images (H&E WSI) by hematopathologists. However, these assessments are subjective and subject to interobserver and intraobserver variability. This may be reduced by using a computer-assisted estimate of bone marrow cellularity. The aim of this study was to develop a fully automated algorithm to estimate bone marrow cellularity in H&E WSI stains using bone marrow segmentation. Data consisted of eight bone marrow H&E WSIs extracted from eight subjects. An algorithm was developed to estimate the bone marrow cellularity consisting of biopsy segmentation, tissue classification, and bone marrow segmentation. Segmentations of the red and yellow bone marrow (YBM) were used to estimate the bone marrow cellularity within the WSI H&E stains. The DICE coefficient between automatic tissue segmentations and ground truth segmentations conducted by an experienced hematopathologist were used for validation. Furthermore, the agreement between the automatic and two manual cellularity estimates was assessed using Bland-Altman plots and intraclass correlation coefficients (ICC). The validation of the bone marrow segmentation demonstrated an average DICE of 0.901 and 0.920 for the red and YBM, respectively. A mean cellularity estimate difference of -0.552 and - 7.816 was obtained between the automatic cellularity estimates and two manual cellularity estimates, respectively. An ICC of 0.980 (95%CI: 0.925-0.995, P-value: 5.51 × 10 ) was obtained between the automatic and manual cellularity estimates based on manual annotations. The study demonstrated that it was possible to obtain bone marrow cellularity estimates with a good agreement with bone marrow cellularity estimates obtained from an experienced hematopathologist. © 2019 International Society for Advancement of Cytometry.
骨髓细胞计数是诊断血液病理学中的一项重要指标。目前，骨髓细胞计数的金标准是血液病理学家对苏木精和伊红染色的全玻片图像（H&E WSI）进行手动检查。然而，这些评估是主观的，存在观察者间和观察者内的变异性。通过使用计算机辅助的骨髓细胞计数估计可以减少这种变异性。本研究的目的是开发一种全自动算法，通过骨髓分割来估计 H&E WSI 染色中的骨髓细胞计数。数据由从 8 名受试者中提取的 8 张骨髓 H&E WSI 组成。开发了一种算法来估计骨髓细胞计数，包括活检分割、组织分类和骨髓分割。使用红骨髓（red bone marrow，RBM）和黄骨髓（yellow bone marrow，YBM）的分割来估计 H&E WSI 染色中的骨髓细胞计数。自动组织分割与经验丰富的血液病理学家进行的地面实况分割之间的 DICE 系数用于验证。此外，还使用 Bland-Altman 图和组内相关系数（intraclass correlation coefficient，ICC）评估自动和两种手动细胞计数估计之间的一致性。骨髓分割的验证结果表明，RBM 和 YBM 的平均 DICE 分别为 0.901 和 0.920。自动细胞计数估计与两种手动细胞计数估计之间的平均细胞计数估计差异分别为-0.552 和-7.816。基于手动注释，自动和手动细胞计数估计之间的 ICC 为 0.980（95%CI：0.925-0.995，P 值：5.51×10）。该研究表明，有可能获得与经验丰富的血液病理学家获得的骨髓细胞计数估计具有良好一致性的骨髓细胞计数估计。
