# Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data

arXiv: https://arxiv.org/abs/1912.08364 · HTML: https://mlsanity.com/paper/1912.08364/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Is attention all you need in medical image analysis? A review](https://mlsanity.com/paper/2307.12775/) (2023)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features](https://mlsanity.com/paper/2303.07852/) (2023)
- [Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound](https://mlsanity.com/paper/2405.09959/) (2024)
- [RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images](https://mlsanity.com/paper/2207.07494/) (2022)
- [Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection](https://mlsanity.com/paper/2505.17683/) (2025)
- [A hybrid Kolmogorov-Arnold network for medical image segmentation](https://mlsanity.com/paper/2602.07702/) (2026)

## Abstract (arXiv)

Automatic segmentation of anatomical landmarks from ultrasound (US) plays an important role in the management of preterm neonates with a very low birth weight due to the increased risk of developing intraventricular hemorrhage (IVH) or other complications. One major problem in developing an automatic segmentation method for this task is the limited availability of annotated data. To tackle this issue, we propose a novel image synthesis method using multi-scale self attention generator to synthesize US images from various segmentation masks. We show that our method can synthesize high-quality US images for every manipulated segmentation label with qualitative and quantitative improvements over the recent state-of-the-art synthesis methods. Furthermore, for the segmentation task, we propose a novel method, called Confidence-guided Brain Anatomy Segmentation (CBAS) network, where segmentation and corresponding confidence maps are estimated at different scales. In addition, we introduce a technique which guides CBAS to learn the weights based on the confidence measure about the estimate. Extensive experiments demonstrate that the proposed method for both synthesis and segmentation tasks achieve significant improvements over the recent state-of-the-art methods. In particular, we show that the new synthesis framework can be used to generate realistic US images which can be used to improve the performance of a segmentation algorithm.