Objective: We present a method and a validation study for the nearly automatic segmentation of liver tumors in CTA scans.
Materials and methods: Our method inputs a liver CTA scan and a small number of user-defined seeds. It first classifies the liver voxels into tumor and healthy tissue classes with an SVM classification engine from which a new set of high- quality seeds is generated. Next, an energy function describing the propagation of these seeds is defined over the 3D image. The functional consists of a set of linear equations that are optimized with the conjugate gradients method. The result is a continuous segmentation map that is thresholded to obtain a binary segmentation.
Results: A retrospective study on a validated clinical dataset consisting of 20 tumors from nine patients' CTA scans from the MICCAI'08 3D Liver Tumors Segmentation Challenge Workshop yielded an average aggregate score of 67, an average symmetric surface distance of 1.76 mm (SD = 0.61 mm) which is better than the 2.0 mm of other methods on the same database, and a comparable volumetric overlap error (33.8 vs. 32.6%). The advantage of our method is that it requires less user interaction compared to other methods.
Conclusion: Our results indicate that our method is accurate, efficient, and robust to wide variety of tumor types and is comparable or superior to other semi-automatic segmentation methods, with much less user interaction.