  Placenta volume measurement from 3D ultrasound images is critical for
predicting pregnancy outcomes, and manual annotation is the gold standard.
However, such manual annotation is expensive and time-consuming. Automated
segmentation algorithms can often successfully segment the placenta, but these
methods may not consistently produce robust segmentations suitable for
practical use. Recently, inspired by the Segment Anything Model (SAM), deep
learning-based interactive segmentation models have been widely applied in the
medical imaging domain. These models produce a segmentation from visual prompts
provided to indicate the target region, which may offer a feasible solution for
practical use. However, none of these models are specifically designed for
interactively segmenting 3D ultrasound images, which remain challenging due to
the inherent noise of this modality. In this paper, we evaluate publicly
available state-of-the-art 3D interactive segmentation models in contrast to a
human-in-the-loop approach for the placenta segmentation task. The Dice score,
normalized surface Dice, averaged symmetric surface distance, and 95-percent
Hausdorff distance are used as evaluation metrics. We consider a Dice score of
0.95 a successful segmentation. Our results indicate that the human-in-the-loop
segmentation model reaches this standard. Moreover, we assess the efficiency of
the human-in-the-loop model as a function of the amount of prompts. Our results
demonstrate that the human-in-the-loop model is both effective and efficient
for interactive placenta segmentation. The code is available at
\url{https://github.com/MedICL-VU/PRISM-placenta}.
