An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
This addresses a domain-specific medical imaging problem for diagnosing heart conditions, but it appears incremental as it builds on existing methods.
The paper tackled the detection of left atrial thrombi using transesophageal echocardiography by extracting local binary pattern variance features and employing multiple-instance learning, achieving better performance than other methods.
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.