On Simulating Subjective Evaluation Using Combined Objective Metrics for Validation of 3D Tumor Segmentation

• DOI: 10.1007/978-3-540-75757-3_118
• Publication Date: 2007-11-21T09:52:10Z
• Authors: Xiang Deng; Lei Zhu; Yiyong Sun; Chenyang Xu; Lan Song; Jiuhong Chen; Reto D. Merges; Marie-Pierre Jolly; Michael Suehling; Xiaodong Xu
• Keywords: Observer Variation; Reproducibility of Results; Models, Biological; Sensitivity and Specificity; Pattern Recognition, Automated; Radiographic Image Enhancement; Imaging, Three-Dimensional; Artificial Intelligence; Neoplasms; Humans; Radiographic Image Interpretation, Computer-Assisted; Computer Simulation; Tomography, X-Ray Computed; Algorithms

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.