Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks.

ABSTRACT: Organ delineation is crucial to diagnosis and therapy, while it is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learning for head-and-neck region on DECT. We proposed a mask scoring regional convolutional neural network (R-CNN) where comprehensive features are firstly learnt from two independent pyramid networks and are then combined via deep attention strategy to highlight the informative ones extracted from both two channels of low and high energy CT. To perform multi-organ segmentation and avoid misclassification, a mask scoring subnetwork was integrated i

SUBMITTER: Wang T 

PROVIDER: S-EPMC11747937 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature