AUTOMATIC BODY LOCALIZATION AND BRAIN VENTRICLE SEGMENTATION IN 3D HIGH FREQUENCY ULTRASOUND IMAGES OF MOUSE EMBRYOS
| Autor: | Jeffrey A. Ketterling, Jen-wei Kuo, Yao Wang, Ziming Qiu, Jonathan Mamou, Daniel H. Turnbull, Orlando Aristizabal |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Similarity (geometry)
Computer science business.industry Ultrasound Pattern recognition 02 engineering and technology Thresholding Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Region of interest Cut 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business High frequency ultrasound Brain Ventricle |
| Zdroj: | ISBI |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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