Ultrasound Scatterer Density Classification Using Convolutional Neural Networks by Exploiting Patch Statistics

TL;DR

This paper introduces a CNN-based method that leverages patch statistics for classifying tissue scatterer density in ultrasound images, outperforming traditional approaches and working across various imaging conditions.

Contribution

The paper presents a novel CNN architecture that uses patch statistics as additional inputs for scatterer density classification, trained on simulation data and validated on experimental and in vivo data.

Findings

CNN outperforms classic models in scatterer density classification

Method works across different imaging parameters without reference phantoms

Patch statistics improve classification accuracy

Abstract

Quantitative ultrasound (QUS) can reveal crucial information on tissue properties such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or low-density scatterers (LDS), respectively. Conventionally, the scatterer density has been classified using estimated statistical parameters of the amplitude of backscattered echoes. However, if the patch size is small, the estimation is not accurate. These parameters are also highly dependent on imaging settings. In this paper, we propose a convolutional neural network (CNN) architecture for QUS, and train it using simulation data. We further improve the network performance by utilizing patch statistics as additional input channels. We evaluate the network using simulation data, experimental phantoms and in vivo data. We also compare our proposed network with different classic and deep learning models, and demonstrate its superior performance in classification of tissues with different scatterer density values. The results also show that the proposed network is able to work with different imaging parameters with no need for a reference phantom. This work demonstrates the potential of CNNs in classifying scatterer density in ultrasound images.