• DOI:10.1109/TUFFC.2022.3144685
• Corpus ID: 246015941

Robust Scatterer Number Density Segmentation of Ultrasound Images

@article{Tehrani2022RobustSN,
  title={Robust Scatterer Number Density Segmentation of Ultrasound Images},
  author={Ali Kafaei Zad Tehrani and Iv{\'a}n M. Rosado-Mendez and Hassan Rivaz},
  journal={IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
  year={2022},
  volume={69},
  pages={1169-1180}
}

• A. K. Z. Tehrani, I. Rosado-Mendez, H. Rivaz
• Published 16 January 2022
• Physics
• IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

Quantitative ultrasound (QUS) aims to reveal information about the tissue microstructure using backscattered echo signals from clinical scanners. Among different QUS parameters, scatterer number density is an important property that can affect the estimation of other QUS parameters. Scatterer number density can be classified into high or low scatterer densities. If there are more than ten scatterers inside the resolution cell, the envelope data are considered as fully developed speckle (FDS) and, otherwise, as underdeveloped speckle (UDS). In conventional methods, the envelope data are divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters, such as SNR and skewness, are employed to classify each patch of envelope data. However, these parameters are system-dependent, meaning that their distribution can change by the imaging settings and patch size. Therefore, reference phantoms that have known scatterer number density are imaged with the same imaging settings to mitigate system dependency. In this article, we aim to segment regions of ultrasound data without any patching. A large dataset is generated, which has different shapes of scatterer number density and mean scatterer amplitude using a fast simulation method. We employ a convolutional neural network (CNN) for the segmentation task and investigate the effect of domain shift when the network is tested on different datasets with different imaging settings. Nakagami parametric image is employed for multitask learning to improve performance. Furthermore, inspired by the reference phantom methods in QUS, a domain adaptation stage is proposed, which requires only two frames of data from FDS and UDS classes. We evaluate our method for different experimental phantoms and in vivo data.