Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Framework for Evaluating Video Object Segmentation Algorithms
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Objective evaluation of video segmentation quality
IEEE Transactions on Image Processing
Perceptually-weighted evaluation criteria for segmentation masks in video sequences
IEEE Transactions on Image Processing
Automating image segmentation verification and validation by learning test oracles
Information and Software Technology
Evaluating segmentation error without ground truth
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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In this paper, we present a new segmentation evaluation method that can simulate radiologist's subjective assessment of 3D tumor segmentation in CT images. The method uses a new metric defined as a linear combination of a set of commonly used objective metrics. The weighing parameters of the linear combination are determined by maximizing the rank correlation between radiologist's subjective rating and objective measurements. Experimental results on 93 lesions demonstrate that the new composite metric shows better performance in segmentation evaluation than each individual objective metric. Also, segmentation rating using the composite metric compares well with radiologist's subjective evaluation. Our method has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale segmentation evaluation studies.