Gandomkar Ziba, Brennan Patrick C, Mello-Thoms Claudia
Medical Image Optimisation and Perception Research Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Australia.
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, USA.
J Pathol Inform. 2017 Sep 7;8:34. doi: 10.4103/jpi.jpi_22_17. eCollection 2017.
Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest.
Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others.
The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as "probably a mitosis" by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using -means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted.
The Kruskal-Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features.
The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects.
Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects.
先前的研究表明，病理学家在识别乳腺切片中的有丝分裂方面的一致性相当一般。
确定乳腺切片中易于识别的有丝分裂、具有挑战性的有丝分裂和误计的非有丝分裂之间显著不同的定量特征，并确定哪种颜色空间比其他颜色空间能更好地捕捉组间差异。
该数据集包含乳腺切片中的453个有丝分裂和265个误计的物体。根据三位注释它们的病理学家的置信度，将有丝分裂分为三类。大多数病理学家注释为“可能是有丝分裂”的有丝分裂被视为具有挑战性的类别。误计的物体仅被一位病理学家识别为有丝分裂或可能是有丝分裂。使用k均值聚类对有丝分裂进行分割，随后进行形态学操作。从分割区域以及八个颜色空间不同通道中63×63像素的图像块中提取形态学、基于强度和纹理特征。还提取了描述每个图像中有丝分裂周围细胞的整体特征。
使用Kruskal-Wallis H检验，随后进行Tukey-Kramer检验来识别显著不同的特征。
结果表明，与其他有丝分裂相比，具有挑战性的有丝分裂更小且更圆。在不同特征中，Gabor纹理特征在具有挑战性的有丝分裂和易于识别的有丝分裂之间的差异比其他特征更大。非有丝分裂的大小与易于识别的有丝分裂相似，但非有丝分裂更圆。来自染色质通道的基于强度的特征是易于识别的有丝分裂和误计物体之间最具区分性的特征。
定量特征可用于描述具有挑战性的有丝分裂和误计的非有丝分裂物体的特征。
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