# Deep Placental Vessel Segmentation for Fetoscopic Mosaicking

arXiv: https://arxiv.org/abs/2007.04349 · HTML: https://mlsanity.com/paper/2007.04349/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image](https://mlsanity.com/paper/2312.07381/) (2023)
- [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024)
- [MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance](https://mlsanity.com/paper/2412.15058/) (2024)
- [FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset](https://mlsanity.com/paper/2106.05923/) (2021)
- [Generative AI Enables Medical Image Segmentation in Ultra Low-Data Regimes](https://mlsanity.com/paper/2408.17421/) (2024)
- [RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation](https://mlsanity.com/paper/2501.08458/) (2025)
- [Real-time Nonrigid Mosaicking of Laparoscopy Images](https://mlsanity.com/paper/2103.07414/) (2021)
- [Novel OCT mosaicking pipeline with Feature- and Pixel-based registration](https://mlsanity.com/paper/2311.13052/) (2023)
- [VesSAM: Efficient Multi-Prompting for Segmenting Complex Vessel](https://mlsanity.com/paper/2511.00981/) (2025)
- [Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings](https://mlsanity.com/paper/2206.12512/) (2022)
- [Learning-Based Keypoint Registration for Fetoscopic Mosaicking](https://mlsanity.com/paper/2207.13185/) (2022)
- [Medical Image Analysis using Deep Relational Learning](https://mlsanity.com/paper/2303.16099/) (2023)
- [Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains](https://mlsanity.com/paper/2512.13534/) (2025)
- [ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation](https://mlsanity.com/paper/2609.31160/) (2026)

## Abstract (arXiv)

During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blood flow in both fetuses. The procedure is challenging due to the mobility of the environment, poor visibility in amniotic fluid, occasional bleeding, and limitations in the fetoscopic field-of-view and image quality. Ideally, anastomotic placental vessels would be automatically identified, segmented and registered to create expanded vessel maps to guide laser ablation, however, such methods have yet to be clinically adopted. We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos. The obtained vessel probability maps provide sufficient cues for mosaicking alignment by registering consecutive vessel maps using the direct intensity-based technique. Experiments on 6 different in vivo fetoscopic videos demonstrate that the vessel intensity-based registration outperformed image intensity-based registration approaches showing better robustness in qualitative and quantitative comparison. We additionally reduce drift accumulation to negligible even for sequences with up to 400 frames and we incorporate a scheme for quantifying drift error in the absence of the ground-truth. Our paper provides a benchmark for fetoscopy placental vessel segmentation and registration by contributing the first in vivo vessel segmentation and fetoscopic videos dataset.