# Deep Learning in Medical Ultrasound Image Segmentation: a Review

arXiv: https://arxiv.org/abs/2002.07703 · HTML: https://mlsanity.com/paper/2002.07703/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation](https://mlsanity.com/paper/2208.06449/) (2022)
- [Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View](https://mlsanity.com/paper/2206.14746/) (2022)
- [Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT](https://mlsanity.com/paper/2103.05546/) (2021)
- [Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images](https://mlsanity.com/paper/2104.03715/) (2021)
- [MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation](https://mlsanity.com/paper/2409.19370/) (2024)
- [BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks](https://mlsanity.com/paper/2310.00919/) (2023)
- [DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation](https://mlsanity.com/paper/2411.11116/) (2024)
- [Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection](https://mlsanity.com/paper/2505.17683/) (2025)

## Abstract (arXiv)

Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventions, image analyzing and visualization. In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first. Secondly, for each group, several current representative algorithms are selected, introduced, analyzed and summarized in detail. In addition, common evaluation methods for image segmentation and ultrasound image segmentation datasets are summarized. Further, the performance of the current methods and their evaluations are reviewed. In the end, the challenges and potential research directions for medical ultrasound image segmentation are discussed.