Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model

PurposeLiver volume segmentation is important in computer assisted diagnosis and therapy planning of liver tumors. Manual segmentation is time-consuming, tedious and error prone, so automated methods are needed. Automatic segmentation of MR images is more challenging than for CT images, so a robust system was developed.MethodsAn intensity-based segmentation method that uses probabilistic model to increase the precision of the segmentation was developed. The model was build based on 60 manually contoured liver CT exams and partitioned into 8 parts according to the (Couinaud) segmental anatomy of the liver. The partitioning allows using different intensity statistics in different parts of the organ, which makes it insensitive to local intensity differences from MR artifacts or pathology. The method employs a modality independent model with registration that exploits some LAVA image characteristics. This dependence can be eliminated to adapt the segmentation method for a wide range of MR images.ResultsThe method was evaluated using eight representative, manually segmented MR LAVA exams. The results show that the method can accurately segment the liver volume despite various MR artifacts and pathology. The evaluation shows that the proposed method provides more precise segmentation (6% average absolute relative volume error) compared with global intensity statistics for the whole organ (20% average absolute relative volume error). The compute time of the method was 30 s in average, which is acceptable for wide range of clinical applications.ConclusionAn automatic method that can segment the liver in contrast-enhanced MR LAVA images was developed and tested. The results demonstrate that the method is feasible, efficient and robust to artifacts and pathology.