Computerized scar detection on renal cortical scintigraphy images

Abstract

Renal cortical scintigraphy is a well-established functional imaging technique for visual analysis of radiopharmaceutical tracer distribution. However, the visual evaluation is subjective, causing interobserver variability, especially in a quantifiable number of scars. The purpose of this study was to develop new computerized methods in renal cortical scintigraphy image interpretation, particularly addressing activity distribution and cortex continuity (scars). The proposed methods involve preprocessing stages of model-based automatic kidney segmentation using active-shape model and image normalization (transforming each kidney image into a standardized image vector). For our previous computer-aided diagnosis scheme, two new image-based features [localized activity drop and principal component analysis (PCA)] were defined. Their performance was evaluated and compared with our previous scheme by using free-response receiver operating characteristic that is in terms of sensitivity (true-positive fraction) and the mean number of false positives (FPs) per image. Clinical tests were conducted in 297 patients (231 normal and 66 abnormal). The PCA-based image feature presented the best scar detection performance, followed by the localized activity drop feature. Both schemes were found to be superior to our previous computer-aided diagnosis scheme. In the PCA-based scheme, for sensitivity of 0.90 (76/84), the mean number of FPs was measured as 4.52 (1343/297). For another setting with reduced sensitivity of 0.79 (66/84), the mean number of FPs improved to 1.21 (360/297). Finally, a decision fusion scheme using 'majority voting' was also proposed, the sensitivity and mean number of FPs of which were measured as 0.83 (70/84) and 1.90 (563/297), respectively. The proposed methods have potential to provide effective second-reader information to nuclear medicine specialists in finding scar regions. Possible ways to improve the FP rate were also proposed.