OBJECTIVES: It is the objective of this study to present the use of knowledge-guided procedures in quantitative image analysis and interpretation in histopathology. METHODS: The knowledge-guided procedures were implemented in the form of N-gram encoding methods for the search and detection of areas of atypicality or abnormality in histopathologic sections; they were implemented as expert system for automated scene segmentation based on an associative network with frames at each node. The extraction of histometric features from the basal cell layer of prostatic lesions is presented as an example of automated image interpretation. RESULTS: Rapid search algorithms for lesion detection were able to identify approximately 90% of areas labelled as atypical or abnormal by visual assessment, in lesions of colon, prostate and breast. Automated segmentation of very complex histopathologic imagery was possible with a success rate of approximately 80-90%, in sections of prostatic and colonic lesions. Histometry of the deterioration of the basal cell layer in prostatic lesions provided a monotonic trend curve suitable for the measurement of progression or regression. CONCLUSIONS: Knowledge-guided procedures bring external information, not offered by the imagery itself, to bear on image processing and image analytic methods. This has enabled automated analysis and interpretation of very complex imagery, such as from cribriform glands, resulting in quantitative diagnostic information.
OBJECTIVES: It is the objective of this study to present the use of knowledge-guided procedures in quantitative image analysis and interpretation in histopathology. METHODS: The knowledge-guided procedures were implemented in the form of N-gram encoding methods for the search and detection of areas of atypicality or abnormality in histopathologic sections; they were implemented as expert system for automated scene segmentation based on an associative network with frames at each node. The extraction of histometric features from the basal cell layer of prostatic lesions is presented as an example of automated image interpretation. RESULTS: Rapid search algorithms for lesion detection were able to identify approximately 90% of areas labelled as atypical or abnormal by visual assessment, in lesions of colon, prostate and breast. Automated segmentation of very complex histopathologic imagery was possible with a success rate of approximately 80-90%, in sections of prostatic and colonic lesions. Histometry of the deterioration of the basal cell layer in prostatic lesions provided a monotonic trend curve suitable for the measurement of progression or regression. CONCLUSIONS: Knowledge-guided procedures bring external information, not offered by the imagery itself, to bear on image processing and image analytic methods. This has enabled automated analysis and interpretation of very complex imagery, such as from cribriform glands, resulting in quantitative diagnostic information.
Authors: Pablo de Gracia; Beatriz I Gallego; Blanca Rojas; Ana I Ramírez; Rosa de Hoz; Juan J Salazar; Alberto Triviño; José M Ramírez Journal: PLoS One Date: 2015-11-18 Impact factor: 3.240