# Combining Bayesian and Deep Learning Methods for the Delineation of the Fan in Ultrasound Images

arXiv: https://arxiv.org/abs/2102.02055 · HTML: https://mlsanity.com/paper/2102.02055/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2021.

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

- [FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning](https://mlsanity.com/paper/2310.12600/) (2023)
- [Leveraging Self-Supervised Learning for Fetal Cardiac Planes Classification using Ultrasound Scan Videos](https://mlsanity.com/paper/2407.21738/) (2024)

## Abstract (arXiv)

Ultrasound (US) images usually contain identifying information outside the ultrasound fan area and manual annotations placed by the sonographers during exams. For those images to be exploitable in a Deep Learning framework, one needs to first delineate the border of the fan which delimits the ultrasound fan area and then remove other annotations inside. We propose a parametric probabilistic approach for the first task. We make use of this method to generate a training data set with segmentation masks of the region of interest (ROI) and train a U-Net to perform the same task in a supervised way, thus considerably reducing computational time of the method, one hundred and sixty times faster. These images are then processed with existing inpainting methods to remove annotations present inside the fan area. To the best of our knowledge, this is the first parametric approach to quickly detect the fan in an ultrasound image without any other information than the image itself.