Thompson D, Bartels P H, Bartels H G, Montironi R
Optical Sciences Center, University of Arizona, Tucson 85721, USA.
Anal Quant Cytol Histol. 1995 Oct;17(5):314-22.
To develop procedures for the segmentation of cribriform prostatic glands.
A knowledge-guided procedure following a model-based reasoning process was developed in the context of a set of interacting expert systems for machine vision in histometry.
With 78 entities in the knowledge file, fully automated, correct segmentation of approximately 70-80% of cribriform glands was attained--i.e., outlining of histologic components agreed with visual assessment. Measurement of gland size, shape, lumen area, number of lumina per gland, epithelial layer thickness, degree of cribriformity and determination of completeness of lining of a gland by a basal cell layer could be taken from the correctly segmented images.
The automated procedure allows a histometric characterization of premalignant and malignant prostatic lesions. Extension of system capabilities to the utilization of spectral information is expected to allow an increase in the correct segmentation rate.
开发筛状前列腺腺体分割程序。
在一组用于组织计量学机器视觉的交互专家系统背景下，开发了一种基于模型推理过程的知识引导程序。
知识文件中有78个实体，实现了约70 - 80%的筛状腺体的全自动、正确分割——即组织学成分的勾勒与视觉评估一致。可从正确分割的图像中测量腺体大小、形状、管腔面积、每个腺体的管腔数量、上皮层厚度、筛状程度以及基底细胞层对腺体衬里完整性的判定。
该自动化程序可对前列腺癌前病变和恶性病变进行组织计量学特征描述。预计将系统功能扩展到利用光谱信息可提高正确分割率。
