InterSliceBoost: A deep-learning solution for segmenting anatomical structures in 3D ultrasound images for cLBP assessment

Chronic lower back pain (cLBP) research often neglects a comprehensive layer-by-layer analysis of anatomical structures. Annotating hundreds of slices in a 3D medical imaging examination for machine learning is time-consuming. To address this, we propose InterSliceBoost, a novel method comprising an inter-slice generator and a semantic segmentation model, designed to generate accurate layer-by-layer masks with minimal manual annotation. Our experiments demonstrated that the segmentation model using InterSliceBoost achieved comparable performance to full annotation when trained on only 33% of the B-mode ultrasound images.