# Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images

arXiv: https://arxiv.org/abs/2407.08020 · HTML: https://mlsanity.com/paper/2407.08020/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM-Med2D](https://mlsanity.com/paper/2308.16184/) (2023)
- [SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images](https://mlsanity.com/paper/2310.15161/) (2023)
- [3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation](https://mlsanity.com/paper/2306.13465/) (2023)
- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models](https://mlsanity.com/paper/2310.19721/) (2023)
- [PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts](https://mlsanity.com/paper/2404.15028/) (2024)
- [False Negative/Positive Control for SAM on Noisy Medical Images](https://mlsanity.com/paper/2308.10382/) (2023)
- [Cats: Complementary CNN and Transformer Encoders for Segmentation](https://mlsanity.com/paper/2208.11572/) (2022)
- [Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View](https://mlsanity.com/paper/2206.14746/) (2022)
- [Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts](https://mlsanity.com/paper/2311.07806/) (2023)
- [Novel OCT mosaicking pipeline with Feature- and Pixel-based registration](https://mlsanity.com/paper/2311.13052/) (2023)
- [Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2404.14837/) (2024)

## Built on by (most cited first, 2 of 2)

- [PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound](https://mlsanity.com/paper/2408.05372/) (2024)
- [Clore: Interactive Pathology Image Segmentation with Click-based Local Refinement](https://mlsanity.com/paper/2603.27625/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/MedICL-VU/PRISM-placenta.