Atlas-based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks.
Atlas-based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks.
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DOI：
10.1007/978-3-030-87202-1_2
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Dawant BM
中科院分区：
文献类型：
--
作者：
Wang J;Su D;Fan Y;Chakravorti S;Noble JH;Dawant BM
We propose an atlas-based method to segment the intracochlear anatomy (ICA) in the post-implantation CT (Post-CT) images of cochlear implant (CI) recipients that preserves the point-to-point correspondence between the meshes in the atlas and the segmented volumes. To solve this problem, which is challenging because of the strong artifacts produced by the implant, we use a pair of co-trained deep networks that generate dense deformation fields (DDFs) in opposite directions. One network is tasked with registering an atlas image to the Post-CT images and the other network is tasked with registering the Post-CT images to the atlas image. The networks are trained using loss functions based on voxel-wise labels, image content, fiducial registration error, and cycle-consistency constraint. The segmentation of the ICA in the Post-CT images is subsequently obtained by transferring the predefined segmentation meshes of the ICA in the atlas image to the Post-CT images using the corresponding DDFs generated by the trained registration networks. Our model can learn the underlying geometric features of the ICA even though they are obscured by the metal artifacts. We show that our end-to-end network produces results that are comparable to the current state of the art (SOTA) that relies on a two-steps approach that first uses conditional generative adversarial networks to synthesize artifact-free images from the Post-CT images and then uses an active shape model-based method to segment the ICA in the synthetic images. Our method requires a fraction of the time needed by the SOTA, which is important for end-user acceptance.
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影响因子：
10.6
作者：
Christensen, GE;Johnson, HJ
通讯作者：
Johnson, HJ
影响因子：
10.9
作者：
Hu Y;Modat M;Gibson E;Li W;Ghavami N;Bonmati E;Wang G;Bandula S;Moore CM;Emberton M;Ourselin S;Noble JA;Barratt DC;Vercauteren T
通讯作者：
Vercauteren T
DOI：
10.1097/mao.0b013e3182a1a7e6
发表时间：
2013-12
期刊：
Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
影响因子：
--
作者：
Pelosi S;Noble JH;Dawant BM;Labadie RF
通讯作者：
Labadie RF
DOI：
10.1109/tbme.2011.2160262
发表时间：
2011-09
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Noble JH;Labadie RF;Majdani O;Dawant BM
通讯作者：
Dawant BM
影响因子：
10.6
作者：
Rueckert, D;Sonoda, LI;Hawkes, DJ
通讯作者：
Hawkes, DJ