Bibbo M, Kim D H, di Loreto C, Dytch H E, Galera-Davidson H, Thompson D, Richards D L, Bartels H G, Bartels P H
Department of Pathology, University of Chicago, Illinois.
Anal Quant Cytol Histol. 1990 Aug;12(4):229-36.
In research for the development of a computer-aided workstation for the objective grading of prostatic carcinoma, tissue architectural (histometric) features were analyzed in ten cases each of well-differentiated, moderately differentiated and poorly differentiated carcinoma (as subjectively graded by the consensus of a panel of experts). Sections were cut at 4 microns, stained by the Feulgen reaction and digitized by two different video-based photometric systems. Some images were interactively segmented, considering the histometric clues to be studied; others were automatically segmented by an expert system-guided technique. The latter procedure produced good results, with over 90% of the nuclei judged to be correctly segmented in 64% of the fields studied and over 80% in another 24% of the fields. While the number of nuclei per field provided some separation of well-differentiated from other lesions, the number of nuclei per gland distinguished between well-differentiated and moderately differentiated lesions. Simplicial decomposition of the images also provided a measure of the degree of differentiation, as did the "texture" of the nuclear placement, based on two run-length statistics. Combination of the run-length features distinguished the three categories of lesions with statistical significance. The results of this study provided insights into the problems (such as the effect of field boundaries) faced in the design of an computer-aided grading system. They also showed the value of expert system-guided scene segmentation and of such histometric features as the field cellularity and the number of nuclei per gland for the discrimination between lesions of different grades of differentiation.
在开发用于前列腺癌客观分级的计算机辅助工作站的研究中，对十例高分化、中分化和低分化癌（由专家小组一致主观分级）的组织结构（组织计量学）特征进行了分析。切片厚度为4微米，采用福尔根反应染色，并通过两种不同的基于视频的光度系统进行数字化处理。一些图像根据要研究的组织计量学线索进行交互式分割；其他图像则通过专家系统引导技术进行自动分割。后一种方法取得了良好的效果，在所研究的64%的视野中，超过90%的细胞核被判定为分割正确，在另外24%的视野中，这一比例超过80%。虽然每个视野中的细胞核数量在一定程度上区分了高分化病变与其他病变，但每个腺体内的细胞核数量则区分了高分化和中分化病变。图像的单纯形分解以及基于两种游程统计的细胞核分布“纹理”也提供了分化程度的度量。游程特征的组合在统计学上显著地区分了三类病变。本研究结果为计算机辅助分级系统设计中面临的问题（如视野边界的影响）提供了见解。它们还显示了专家系统引导的场景分割以及诸如视野细胞密度和每个腺体内的细胞核数量等组织计量学特征在区分不同分化等级病变方面的价值。
