Hot spot detection for breast cancer in Ki-67 stained slides: Image dependent filtering approach

We present a new method to detect hot spots from breast cancer slides stained for Ki67 expression. It is common practice to use centroid of a nucleus as a surrogate representation of a cell. This often requires the detection of individual nuclei. Once all the nuclei are detected, the hot spots are detected by clustering the centroids. For large size images, nuclei detection is computationally demanding. Instead of detecting the individual nuclei and treating hot spot detection as a clustering problem, we considered hot spot detection as an image filtering problem where positively stained pixels are used to detect hot spots in breast cancer images. The method first segments the Ki-67 positive pixels using the visually meaningful segmentation (VMS) method that we developed earlier. Then, it automatically generates an image dependent filter to generate a density map from the segmented image. The smoothness of the density image simplifies the detection of local maxima. The number of local maxima directly corresponds to the number of hot spots in the breast cancer image. The method was tested on 23 different regions of interest images extracted from 10 different breast cancer slides stained with Ki67. To determine the intra-reader variability, each image was annotated twice for hot spots by a boardcertified pathologist with a two-week interval in between her two readings. A computer-generated hot spot region was considered a true-positive if it agrees with either one of the two annotation sets provided by the pathologist. While the intra-reader variability was 57%, our proposed method can correctly detect hot spots with 81% precision.