Plane Localization in 3-D Fetal Neurosonography for Longitudinal Analysis of the Developing Brain.

The parasagittal (PS) plane is a 2-D diagnostic plane used routinely in cranial ultrasonography of the neonatal brain. This paper develops a novel approach to find the PS plane in a 3-D fetal ultrasound scan to allow image-based biomarkers to be tracked from prebirth through the first weeks of postbirth life. We propose an accurate plane-finding solution based on regression forests (RF). The method initially localizes the fetal brain and its midline automatically. The midline on several axial slices is used to detect the midsagittal plane, which is used as a constraint in the proposed RF framework to detect the PS plane. The proposed learning algorithm guides the RF learning method in a novel way by: 1) using informative voxels and voxel informative strength as a weighting within the training stage objective function, and 2) introducing regularization of the RF by proposing a geometrical feature within the training stage. Results on clinical data indicate that the new automated method is more reproducible than manual plane finding obtained by two clinicians

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.

Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer's eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts