Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes
Other conference paper


摘要Volumetric 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.
著者Wang X., Yang X., Dou H., Li S., Heng P. A., Ni D.
會議名稱2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
會議地點Chicago, USA
期刊名稱2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
會議論文集題名2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
關鍵詞prenatal ultrasound, volumetric segmentation, landmark localization, deep learning, adversarial learning

上次更新時間 2021-11-09 於 00:12