title: Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification.
abstract: In this paper, a specific method is presented to facilitate the semi-automatic segmentation of liver tumors and liver metastases in CT images. Accurate and reliable segmentation of tumors is essential for the follow-up of cancer treatment. The core of the algorithm is a level set method. The initialization is generated by a spiral-scanning technique based on dynamic programming. The level set evolves according to a speed image that is the result of a statistical pixel classification algorithm with supervised learning. This method is tested on CT images of the abdomen and compared with manual delineations of liver tumors. The described method outperformed the semi-automatic methods of the other participants of the "3D Liver Tumor Segmentation Challenge 2008". Evaluating the algorithm on the provided test data leads to an average overlap error of 32.6% and an average volume difference of 17.9%. The average, the RMS and the maximum surface distance are 2.0, 2.6 and 10.1 mm, respectively.
authors.affiliation: Medical Image Computing (ESAT/PSI), Faculty of Engineering, Katholieke Universiteit Leuven, University Hospitals Leuven, Medical Imaging Research Center, Herestraat 49 Bus 7003, B-3000 Leuven, Belgium. dirk.smeets@uz.kuleuven.ac.be
authors.forename: Dirk
authors.initials: D
authors.lastname: Smeets
authors.forename: Dirk
authors.initials: D
authors.lastname: Loeckx
authors.forename: Bert
authors.initials: B
authors.lastname: Stijnen
authors.forename: Bart
authors.initials: B
authors.lastname: De Dobbelaer
authors.forename: Dirk
authors.initials: D
authors.lastname: Vandermeulen
authors.forename: Paul
authors.initials: P
authors.lastname: Suetens
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D017143:Fuzzy Logic; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D036542:Tomography, Spiral Computed / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*