PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.
PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ensuring an exhaustive analysis of intricate 3D pelvi...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 957 - 967 |
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| Autores principales: | , , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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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=184081750&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081750 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081750 184081750 189894378 184081750 10.1007/s10278-024-01249-w 184081750 ppf: 957 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration. aug: au: Zakaria, Rguibi Abdelmajid, Hajami Dya, Zitouni Hakim, Allali affil: https://ror.org/03cdvht47 LAVETE Laboratory, Hassan First University, Settat, Morocco sug: subj: Image Processing, Computer Assisted Methods Neural Networks (Computer) Imaging, Three-Dimensional Methods Pelvis Radiography Pelvis Anatomy and Histology Collaboration Human Male Female Adult Decision Making, Clinical Validity Architecture Radiation Dosage Reinforcement (Psychology) Deep Learning Adult: 19-44 years Male Female ab: PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ensuring an exhaustive analysis of intricate 3D pelvic structures. The architecture combines max pooling, parametric ReLU activations, and agent-specific layers to optimize both individual and collective decision-making processes. A communication mechanism efficiently aggregates outputs from these shared layers, enabling agents to make well-informed decisions by harnessing combined intelligence. PelviNet's evaluation centers on both quantitative accuracy metrics and visual representations to elucidate agents' performance in pinpointing optimal landmarks. Empirical results demonstrate PelviNet's superiority over traditional methods, achieving an average image-wise error of 2.8 mm, a subject-wise error of 3.2 mm, and a mean Euclidean distance error of 3.0 mm. These quantitative results highlight the model's efficiency and precision in landmark identification, crucial for medical contexts such as radiation therapy, where exact landmark identification significantly influences treatment outcomes. By reliably identifying critical structures, PelviNet advances pelvic image analysis and offers potential enhancements for broader medical imaging applications, marking a significant step forward in computational healthcare. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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