DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.
Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. To overcome the small size of the hippocampus and the low contrast of MR images, a dual multilevel constrained attention GAN for MRI-based hippocampus segmentation is proposed in...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 6; pp. 2532 - 2554 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173050930&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173050930 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2023 vid: 36 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173050930 172268668 173050930 173050930 10.1007/s10278-023-00854-5 173050930 ppf: 2532 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation. aug: au: Chen, Xue Peng, Yanjun Li, Dapeng Sun, Jindong affil: https://ror.org/04gtjhw98 College of Computer Science and Engineering, Shandong University of Science and Technology, 266590, Qingdao, Shandong, China sug: subj: Hippocampus Physiology Hippocampus Radiography Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Learning Methods Human China Male Female Adult Middle Age Descriptive Statistics Diffusion of Innovation Diagnostic Imaging Methods Algorithms Image Interpretation, Computer Assisted Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. To overcome the small size of the hippocampus and the low contrast of MR images, a dual multilevel constrained attention GAN for MRI-based hippocampus segmentation is proposed in this paper, which is used to provide a relatively effective balance between suppressing noise interference and enhancing feature learning. First, we design the dual-GAN backbone to effectively compensate for the spatial information damage caused by multiple pooling operations in the feature generation stage. Specifically, dual-GAN performs joint adversarial learning on the multiscale feature maps at the end of the generator, which yields an average Dice coefficient (DSC) gain of 5.95% over the baseline. Next, to suppress MRI high-frequency noise interference, a multilayer information constraint unit is introduced before feature decoding, which improves the sensitivity of the decoder to forecast features by 5.39% and effectively alleviates the network overfitting problem. Then, to refine the boundary segmentation effects, we construct a multiscale feature attention restraint mechanism, which forces the network to concentrate more on effective multiscale details, thus improving the robustness. Furthermore, the dual discriminators D1 and D2 also effectively prevent the negative migration phenomenon. The proposed DMCA-GAN obtained a DSC of 90.53% on the Medical Segmentation Decathlon (MSD) dataset with tenfold cross-validation, which is superior to the backbone by 3.78%. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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