Low-limb muscles segmentation in 3D freehand ultrasound using non-learning methods and label transfer

vanessa Gonzalez Duque, Dawood AL CHANTI, Marion Crouzier, Antoine Nordez, Lilian Lacourpaille, Diana Mateus

Abstract: In this paper, we aim to assist the measurement of the volume of lower-limb muscles from 3D freehand ultrasound images. Volume estimation typically requires a very time-consuming manual segmentation step. To facilitate and speed-up the volume measurements, in this paper, we propose a non-learning based approach that, starting from sparse annotations in 2D images, propagates the labels to the full volume. Furthermore, we rely on 3D3D image-based registration to combine labels from different ultrasound acquisitions of the same muscle with different qualities. Our goal here is to provide a simple and low-cost solution, relying mainly on open-source software for the processing steps. The proposed approach effectively reduces the manual interaction time while providing reasonable estimations for the segmentation and volume calculation. We achieve a mean dice score of 0.89±0.03 and a mean volumetric measure error of 4,18%. The resultant volumes may also be useful for building augmented annotated databases to develop automatic learning-based segmentation approaches.