Deep Mouse: An End-To-End Auto-Context Refinement Framework for Brain Ventricle & Body Segmentation in Embryonic Mice Ultrasound Volumes

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 re?nement framework, consisting of two stages. The ?rst 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 re?nement segmentation network. Joint training of the two stages provides signi?cant improvement in Dice Similarity Coef?cient (DSC) over using only the ?rst stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method signi?cantly 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.
  • IEEE MemberUS $11.00
  • Society MemberUS $0.00
  • IEEE Student MemberUS $11.00
  • Non-IEEE MemberUS $15.00
Purchase

Videos in this product

Deep Mouse: An End-To-End Auto-Context Refinement Framework for Brain Ventricle & Body Segmentation in Embryonic Mice Ultrasound Volumes

00:11:50
0 views
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 re?nement framework, consisting of two stages. The ?rst 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 re?nement segmentation network. Joint training of the two stages provides signi?cant improvement in Dice Similarity Coef?cient (DSC) over using only the ?rst stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method signi?cantly 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.