Guideline-based machine learning for standard plane extraction in 3D cardiac ultrasound

Abstract

The extraction of six standard planes in 3D cardiac ultrasound plays an important role in clinical examination to analyze cardiac function. This paper proposes a guideline-based machine learning method for efficient and accurate standard plane extraction. A cardiac ultrasound guideline determines appropriate operation steps for clinical examinations. The idea of guideline-based machine learning is incorporating machine learning approaches into each stage of the guideline. First, Hough forest with hierarchical search is applied for 3D feature point detection. Second, initial planes are determined using anatomical regularities according to the guideline. Finally, a regression forest integrated with constraints of plane regularities is applied for refining each plane. The proposed method was evaluated on a 3D cardiac ultrasound dataset. Compared with other plane extraction methods, it demonstrated an improved accuracy with a significantly faster running time of 0.8 s per volume.