TY - GEN
T1 - Automatic body localization and brain ventricle segmentation in 3D high frequency ultrasound images of mouse embryos
AU - Kuo, Jen Wei
AU - Qiu, Ziming
AU - Aristizabal, Orlando
AU - Mamou, Jonathan
AU - Turnbull, Daniel H.
AU - Ketterling, Jeffrey
AU - Wang, Yao
N1 - Funding Information:
High-frequency ultrasound (HFU) has become an effective imaging tool for the rapid phenotyping of mouse embryos due to its fast 3D data-acquisition capability and the availability of commercial and research ultrasound scanners [2]. Fig. 1(a) and (b) display selected slices of two HFU image volumes of mouse embryos, one manually truncated to The research described in this paper was supported in part by NIH grant EB022950.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segments the ROI into BVs, the body, the amniotic fluid, and the uterine wall, using nested graph cut. Simultaneously multilevel thresholding is applied to the whole-body image to propose candidate BV components. These candidates are further truncated by the embryo mask (body+BVs) to refine the BV candidates. Finally, subsets of all candidate BVs are compared with pre-trained spring models describing valid BV structures, to identify true BV components. The system can segment the body accurately in most cases based on visual inspection, and achieves average Dice similarity coefficient of 0.8924 ± 0.043 for the BVs on 36 HFU image volumes.
AB - This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segments the ROI into BVs, the body, the amniotic fluid, and the uterine wall, using nested graph cut. Simultaneously multilevel thresholding is applied to the whole-body image to propose candidate BV components. These candidates are further truncated by the embryo mask (body+BVs) to refine the BV candidates. Finally, subsets of all candidate BVs are compared with pre-trained spring models describing valid BV structures, to identify true BV components. The system can segment the body accurately in most cases based on visual inspection, and achieves average Dice similarity coefficient of 0.8924 ± 0.043 for the BVs on 36 HFU image volumes.
KW - Brain ventricle segmentation
KW - Graph cut
KW - High-frequency ultrasound
KW - Localization
KW - Mouse embryo
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U2 - 10.1109/ISBI.2018.8363655
DO - 10.1109/ISBI.2018.8363655
M3 - Conference contribution
AN - SCOPUS:85048133299
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 635
EP - 639
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -