  Automatic image segmentation is a critical component of medical image
analysis, and hence quantifying segmentation performance is crucial. Challenges
in medical image segmentation are mainly due to spatial variations of regions
to be segmented and imbalance in distribution of classes. Commonly used metrics
treat all detected pixels, indiscriminately. However, pixels in smaller
segments must be treated differently from pixels in larger segments, as
detection of smaller ones aid in early treatment of associated disease and are
also easier to miss. To address this, we propose a novel evaluation metric for
segmentation performance, emphasizing smaller segments, by assigning higher
weightage to smaller segment pixels. Weighted false positives are also
considered in deriving the new metric named, "SSEGEP"(Small SEGment Emphasized
Performance evaluation metric), (range : 0(Bad) to 1(Good)). The experiments
were performed on diverse anatomies(eye, liver, pancreas and breast) from
publicly available datasets to show applicability of the proposed metric across
different imaging techniques. Mean opinion score (MOS) and statistical
significance testing is used to quantify the relevance of proposed approach.
Across 33 fundus images, where the largest exudate is 1.41%, and the smallest
is 0.0002% of the image, the proposed metric is 30% closer to MOS, as compared
to Dice Similarity Coefficient (DSC). Statistical significance testing resulted
in promising p-value of order 10^{-18} with SSEGEP for hepatic tumor compared
to DSC. The proposed metric is found to perform better for the images having
multiple segments for a single label.

Image and Video Processing;Computer Vision and Pattern Recognition