DEEP MOUSE: AN END-TO-END AUTO-CONTEXT REFINEMENT FRAMEWORK FOR BRAIN VENTRICLE & BODY SEGMENTATION IN EMBRYONIC MICE ULTRASOUND VOLUMES.
Proc IEEE Int Symp Biomed Imaging
; 2020: 122-126, 2020 Apr.
Article
em En
| MEDLINE
| ID: mdl-33381278
ABSTRACT
The segmentation of the brain ventricle (BV) and body in embryonic mice high-frequency ultrasound (HFU) volumes can provide useful information for biological researchers. However, manual segmentation of the BV and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume ≈1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Proc IEEE Int Symp Biomed Imaging
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos