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

Vanessa Gonzalez Duque; Dawood Alchanti; Marion Crouzier; Antoine Nordez; Lilian Lacourpaille; Diana Mateus

doi:10.1117/12.2575991

3 November 2020 Low-limb muscles segmentation in 3D freehand ultrasound using non-learning methods and label transfer

Vanessa Gonzalez Duque, Dawood Alchanti, Marion Crouzier, Antoine Nordez, Lilian Lacourpaille, Diana Mateus

Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830J (2020) https://doi.org/10.1117/12.2575991
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru

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.

Citation Download Citation

Vanessa Gonzalez Duque, Dawood Alchanti, Marion Crouzier, Antoine Nordez, Lilian Lacourpaille, and Diana Mateus "Low-limb muscles segmentation in 3D freehand ultrasound using non-learning methods and label transfer", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830J (3 November 2020); https://doi.org/10.1117/12.2575991

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