Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes
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AbstractVolumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Segmentation and landmark localization are two key techniques in making the quantitative evaluation of prenatal ultrasound volumes available in clinic. However, both tasks are non-trivial when considering the poor image quality, boundary ambiguity and anatomical variations in volumetric ultrasound. In this paper, we propose an effective framework for simultaneous segmentation and landmark localization in prenatal ultrasound volumes. The proposed framework has two branches where informative cues of segmentation and landmark localization can be propagated bidirectionally to benefit both tasks. As landmark localization tends to suffer from false positives, we propose a distance based loss to suppress the noise and thus enhance the localization map and in turn the segmentation. Finally, we further leverage an adversarial module to emphasize the correspondence between segmentation and landmark localization. Extensively validated on a volumetric ultrasound dataset of fetal femur, our proposed framework proves to be a promising solution to facilitate the interpretation of prenatal ultrasound volumes.
All Author(s) ListWang X., Yang X., Dou H., Li S., Heng P. A., Ni D.
Name of Conference2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Start Date of Conference19/05/2019
End Date of Conference22/05/2019
Place of ConferenceChicago, USA
Country/Region of ConferenceUnited States of America
Journal name2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Proceedings Title2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Year2019
LanguagesEnglish-United States
Keywordsprenatal ultrasound, volumetric segmentation, landmark localization, deep learning, adversarial learning

Last updated on 2021-10-04 at 23:55