Abstract
The Sørensen-Dice similarity coefficient (DSC) is the most common evaluation metric used for image segmentation but it is not always ideal. Namely, the DSC values only depend on the number of misplaced elements instead of their location with respect to the correct segments. Because of this, the DSC is ill-suited for such tasks where the correct location of the borders of an object is difficult to define in an objective way, as is the case in tumor segmentation in positron emission tomography (PET) images. To avoid this issue, we introduce two different modifications of the DSC, one with weights and one with an additional loss term, which also evaluate the distance between the real and the predicted segments. We computed the values of DSC and our new coefficient from 191 predicted tumor segmentation masks created by using PET images of 89 head and neck squamous cell carcinoma patients. We compared the values of all three coefficients with the scores given to these masks by human evaluators. According to our results, the weighted modification of DSC had a higher correlation with the scores given by the human evaluators than the original DSC, and it also produced significantly less variation within the two highest score classes (p-value ≤ 0.018). The new weighted coefficient introduced here has much potential in the evaluation of segmentation results from medical imaging.