Automatic localization of the fetal cerebellum on 3D ultrasound volumes
Assessment of the fetal cerebellar volume on 3D ultrasound data sets is very important in the clinical evaluation of the fetal growth and health. However, the irregular shape of the cerebellum and the strong artifacts of ultrasound images complicate the segmentation without manual intervention. In this paper, the authors propose an approach to locate the cerebellum automatically, which is considered as a prework of the segmentation.
The authors present a weighted Hough transform and a constrained randomized Hough transform to detect the fetal brain midline and the skull, respectively. By combining the location information of these two structures with local image features, a constrained probabilistic boosting tree is then proposed to search the cerebellum.
This algorithm was tested on ultrasound volumes of the fetal head with the gestational age ranging from 20 to 33 weeks. Compared with manual measurements, this algorithm obtained a satisfactory performance with the mean Dice similarity coefficient of 0.92 and the average processing time of 0.75 s per case.
The results demonstrate that the proposed method is an automatic, fast, and accurate tool for searching the fetal cerebellum on ultrasound volumes.