Towards Automatic Semantic Segmentation in Volumetric Ultrasound
Refereed conference paper presented and published in conference proceedings


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Abstract3D ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. However, lacking of efficient tools to decompose the volumetric data greatly limits its widespread. In this paper, we are looking at the problem of volumetric segmentation in ultrasound to promote the volume-based, precise maternal and fetal health monitoring. Our contribution is threefold. First, we propose the first and fully automatic framework for the simultaneous segmentation of multiple objects, including fetus, gestational sac and placenta, in ultrasound volumes, which remains as a rarely-studied but great challenge. Second, based on our customized 3D Fully Convolutional Network, we propose to inject a Recurrent Neural Network (RNN) to flexibly explore 3D semantic knowledge from a novel, sequential perspective, and therefore significantly refine the local segmentation result which is initially corrupted by the ubiquitous boundary uncertainty in ultrasound volumes. Third, considering sequence hierarchy, we introduce a hierarchical deep supervision mechanism to effectively boost the information flow within RNN and further improve the semantic segmentation results. Extensively validated on our in-house large datasets, our approach achieves superior performance and presents to be promising in boosting the interpretation of prenatal ultrasound volumes. Our framework is general and can be easily extended to other volumetric ultrasound segmentation tasks.
All Author(s) ListXin Yang, Lequan Yu, Shengli Li, Xu Wang, Na Wang, Jing Qin, Dong Ni, Pheng-Ann Heng
Name of Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Start Date of Conference11/09/2017
End Date of Conference13/09/2017
Place of ConferenceQuebec
Country/Region of ConferenceCanada
Proceedings TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Year2017
Volume Number10433
PublisherSpringer
Pages711 - 719
ISBN9783319661810
ISSN0302-9743
LanguagesEnglish-United States

Last updated on 2020-05-08 at 03:28