A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points

The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI = 97.6 ± 0.5%; false positive error, FPE = 2.2 ± 0.7%; false negative error, FNE = 2.5. ±. 0.8%; average symmetric surface distance, ASD = 1.4. ±. 0.5. mm) than the 2D (SI = 94.0. ±. 1.9%; FPE = 5.3. ±. 1.1%; FNE = 6.5. ±. 3.7%; ASD = 6.7. ±. 3.8. mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77. ±. 10. s) is significantly less than the 2D region growing method (575. ±. 136. s). The interaction time per CT dataset between the user and a computer of the hybrid method (28. ±. 4. s) is significantly shorter than the 2D region growing method (484. ±. 126. s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.