A comprehensive survey to study the utilities of image segmentation methods in clinical routine

Abstract

The clinicians usually desire to know the shape of the liver during treatment planning to minimize the damage to the surrounding healthy tissues and hepatic vessels, thus, building the geometric model of the liver becomes paramount. There have been several liver image segmentation methods to build the model over the years. Considering the advantages of conventional image segmentation methods, this paper reviews them that spans over last 2 decades. The review examines about twenty-five automated and eleven semi-automatic approaches that include Probabilistic atlas, K-means, Model and knowledge-based (such as active appearance model, live wire), Graph cut, Region growing, Active contour-based, Expectation Maximization-based, Level sets, Laplacian network optimization, etc. The main contribution of this paper is to highlight their clinical suitability by providing their advantages and possible limitations. It is nearly impossible to assess the methodologies on a single scale because a common patient database is usually not used, rather, diverse datasets such as MICCAI 2007 Grand Challenge (Sliver), 3DIRCADb, Zhu Jiang Hospital of Southern Medical University (China) and others have been used. As a result, this study depends on the popular metrics such as FPR, FNR, AER, JCS, ASSD, DSC, VOE, and RMSD. offering a sense of efficacy of each approach. It is found that while automatic segmentation methods perform better technically, they are usually less preferred by the clinicians. Since the objective of this paper is to provide a holistic view of all the conventional methods from clinicians’ stand point, we have suggested a conventional framework based on the findings in this paper. We have also included a few research challenges that the readers could find them interesting.