Comparison of semi-automated image analysis and manual methods for tissue quantification in pancreatic carcinoma

Objective measurements of tissue area during histological examination of carcinoma can yield valuable prognostic information. However, such measurements are not made routinely because the current manual approach is time consuming and subject to large statistical sampling error. In this paper, a semi-automated image analysis method for measuring tissue area in histological samples is applied to the measurement of stromal tissue, cell cytoplasm and lumen in samples of pancreatic carcinoma and compared with the standard manual point counting method. Histological samples from 26 cases of pancreatic carcinoma were stained using the sirius red, light-green method. Images from each sample were captured using two magnifications. Image segmentation based on colour cluster analysis was used to subdivide each image into representative colours which were classified manually into one of three tissue components. Area measurements made using this technique were compared to corresponding manual measurements and used to establish the comparative accuracy of the semi-automated image analysis technique, with a quality assurance study to measure the repeatability of the new technique. For both magnifications and for each tissue component, the quality assurance study showed that the semi-automated image analysis algorithm had better repeatability than its manual equivalent. No significant bias was detected between the measurement techniques for any of the comparisons made using the 26 cases of pancreatic carcinoma. The ratio of manual to semi-automatic repeatability errors varied from 2.0 to 3.6. Point counting would need to be increased to be between 400 and 1400 points to achieve the same repeatability as for the semi-automated technique. The results demonstrate that semi-automated image analysis is suitable for measuring tissue fractions in histological samples prepared with coloured stains and is a practical alternative to manual point Counting.