Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes
This work addresses the need for automated analysis of prenatal ultrasound volumes to improve clinical efficiency, though it is incremental as it builds on existing segmentation and localization methods.
The authors tackled the problem of simultaneously segmenting and localizing landmarks in fetal femur ultrasound volumes, which is challenging due to poor image quality and anatomical variations. Their proposed bidirectional framework with a distance-based loss and adversarial module achieved promising results, as validated on a volumetric ultrasound dataset.
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.