An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography

TL;DR

This paper presents a novel algorithm for detecting left atrial thrombi in transesophageal echocardiography images using local binary pattern variance features and multiple-instance learning, improving detection accuracy.

Contribution

It introduces a new combination of LBPV features and MIL for more effective thrombi detection in TEE images.

Findings

Achieved better detection performance than existing methods.

Demonstrated effectiveness of LBPV features in thrombi detection.

Validated approach with experimental results.

Abstract

Transesophageal echocardiography (TEE) is widely used to detect left atrium (LA)/left atrial appendage (LAA) thrombi. In this paper, the local binary pattern variance (LBPV) features are extracted from region of interest (ROI). And the dynamic features are formed by using the information of its neighbor frames in the sequence. The sequence is viewed as a bag, and the images in the sequence are considered as the instances. Multiple-instance learning (MIL) method is employed to solve the LAA thrombi detection. The experimental results show that the proposed method can achieve better performance than that by using other methods.