Methods of automatic segmentation of anatomy in artifact affected ct images with deep neural networks and applications of same

A method for segmentation of structures of interest (SOI) in a computed tomography (CT) image post-operatively acquired from an implant user in a region of interest in which an implant is implanted, comprising:

• providing an atlas image, a dataset and networks, wherein the dataset comprises a plurality of CT image pairs, randomly partitioned into a training set, a validation set, and a testing set, wherein each CT image pair has a pre-implantation CT (Pre-CT) image and a post-implantation CT (Post-CT) image respectively acquired in a region of a respective implant recipient before and after an implant is implanted in the region, so that the Pre-CT image and the Post-CT image of each CT image pair are an artifact-free CT image and an artifact-affected CT image, respectively;
• wherein the atlas image is a Pre-CT image of the region of a subject that is not in the plurality of CT image pairs;
• and wherein the networks comprises a first network for registering the atlas image to each Post-CT image and a second network for registering each Post-CT image to the atlas image;
• training the networks with the plurality of CT image pairs, so as to learn to register the artifact-affected CT images and the atlas image with assistance of the paired artifact-free CT images;
• inputting the CT image post-operatively acquired from the implant user to the trained networks to generate a dense deformation field (DDF) from the input Post-CT image to the atlas image;
• and
• warping a segmentation mesh of the SOI in the atlas image to the input Post-CT image using the DDF so as to generate the segmentation mesh of the SOI in the input Post-CT image, wherein the segmentation mesh of the SOI in the atlas image is generated by applying an active shape model-based (ASM) method to the atlas image.