Minimally interactive placenta segmentation from three-dimensional ultrasound images

Abstract

Purpose: Placental size in early pregnancy has been associated with important clinical outcomes, including fetal growth. However, extraction of placental size from three-dimensional ultrasound (3DUS) requires time-consuming interactive segmentation methods and is prone to user variability. We propose a semiautomated segmentation technique that requires minimal user input to robustly measure placental volume from 3DUS images. Approach: For semiautomated segmentation, a single, central 2D slice was manually annotated to initialize an automated multi-atlas label fusion (MALF) algorithm. The dataset consisted of 47 3DUS volumes obtained at 11 to 14 weeks in singleton pregnancies (28 anterior and 19 posterior). Twenty-six of these subjects were imaged twice within the same session. Dice overlap and surface distance were used to quantify the automated segmentation accuracy compared to expert manual segmentations. The mean placental volume measurements obtained by our method and VOCAL (virtual organ computer-aided analysis), a leading commercial semiautomated method, were compared to the manual reference set. The test-retest reliability was also assessed. Results: The overlap between our automated segmentation and manual (mean Dice: $0.824\pm 0.061$ , median: 0.831) was within the range reported by other methods requiring extensive manual input. The average surface distance was $1.66\pm 0.96\mathrm{mm}$ . The correlation coefficient between test-retest volumes was $r=0.88$ , and the intraclass correlation was $\mathrm{ICC}\left(1\right)=0.86$ . Conclusions: MALF is a promising method that can allow accurate and reliable segmentation of the placenta with minimal user interaction. Further refinement of this technique may allow for placental biometry to be incorporated into clinical pregnancy surveillance.

Keywords: image processing; image segmentation; multi-atlas label fusion; placenta; ultrasound.