RESUMO
In recent years, fast and precise lumbar vertebrae segmentation technology have been one of the important topics in practical medical diagnosis and assisted medical surgery scenarios. However, most of the existing vertebral segmentation methods are based on the whole vertebral scanning space, which, up to some extent, is difficult to meet the clinical needs because of its large time complexity and space complexity. Different from the existing methods, for better exploiting the real time of lumbar segmentation, meanwhile ensuring its accuracy, a novel 3D lumbar vertebrae location and segmentation method based on the fusion envelope of 2D hybrid visual projection images (LVLS-HVPFE) is proposed in this paper. Firstly, a 2D projection location network of lumbar vertebrae based on fusion envelope of hybrid visual projection images is proposed to obtain the accurate location of each intact lumbar vertebra in the coronal and sagittal planes respectively. Among them, the envelope dataset of hybrid visual projection images (EDHVPs) is established to enhance feature representation and suppress interference in the process of dimensionality reduction projection. An envelope deep neural network (EDNN) for EDHVPs is established to effectively obtain depth envelope structure features with three different sizes, and a dimension reduction fusion mechanism is proposed to increase the sampling density of features and ensure the mutual independence of multi-scale features. Secondly, the concept of 3D localization criterion with spatial dimensionality reduction (SDRLC) is first proposed as a measure to verify the distribution consistency of vertebral targets in coronal and sagittal planes of a CT scan, and it can directionally guide for the subsequent 3D lumbar segmentation. Thirdly, under the condition of 3D positioning subspace of each intact lumbar vertebra, the 3D segmentation network based on spatial orientation guidance is used to realize an accurate segmentation of corresponding lumbar vertebra. The proposed method is evaluated with three representative datasets, and experimental results show that it is superior to the state-of-the-art methods.
