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

计算机科学 卷积神经网络 人工智能 分割 Sørensen–骰子系数 深度学习 感兴趣区域 模式识别(心理学) 图像分割 计算机视觉 棱锥(几何) 数学 几何学
作者
Tonghe Wang,Yang Lei,Justin Roper,Beth Ghavidel,Jonathan J. Beitler,Mark W. McDonald,Walter J. Curran,Tian Liu,Xiaofeng Yang
出处
期刊:Physics in Medicine and Biology [IOP Publishing]
卷期号:66 (11): 115008-115008 被引量:16
标识
DOI:10.1088/1361-6560/abfce2
摘要

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 into the Mask R-CNN framework to build the correlation between the class of potential detected organ's region-of-interest (ROI) and the shape of that organ's segmentation within that ROI. We evaluated our model on DECT images from 127 head-and-neck cancer patients (66 training, 61 testing) with manual contours of 19 organs as training target and ground truth. For large- and mid-sized organs such as brain and parotid, the proposed method successfully achieved average Dice similarity coefficient (DSC) larger than 0.8. For small-sized organs with very low contrast such as chiasm, cochlea, lens and optic nerves, the DSCs ranged between around 0.5 and 0.8. With the proposed method, using DECT images outperforms using SECT in almost all 19 organs with statistical significance in DSC (
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