Enhanced abdominal multi-organ segmentation with 3D UNet and UNet + + deep neural networks utilizing the MONAI framework.
Accurate segmentation of organs in the abdomen is a primary requirement for any medical analysis and treatment planning. In this study, we propose an approach based on 3D UNet and UNet + + architectures implemented in the MONAI framework for addressing challenges that arise due to anatomical variabi...
| Publicado en: | Abdominal Radiology Vol. 51; no. 2; pp. 954 - 977 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2026
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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=191808251&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191808251 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2026 vid: 51 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191808251 186279065 10.1007/s00261-025-05041-4 191808251 ppf: 954 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Enhanced abdominal multi-organ segmentation with 3D UNet and UNet + + deep neural networks utilizing the MONAI framework. aug: au: Tejashwini, P. S. Thriveni, J. Venugopal, K. R. affil: University of Visvesvaraya College of Engineering, Bengaluru, India sug: ab: Accurate segmentation of organs in the abdomen is a primary requirement for any medical analysis and treatment planning. In this study, we propose an approach based on 3D UNet and UNet + + architectures implemented in the MONAI framework for addressing challenges that arise due to anatomical variability, complex shape rendering of organs, and noise in CT/MRI scans. The models can analyze information in three dimensions from volumetric data, making use of skip and dense connections, and optimizing the parameters using Secretary Bird Optimization (SBO), which together help in better feature extraction and boundary delineation of the structures of interest across sets of multi-organ tissues. The developed model's performance was evaluated on multiple datasets, ranging from Pancreas-CT to Liver-CT and BTCV. The results indicated that on the Pancreas-CT dataset, a DSC of 94.54% was achieved for 3D UNet, while a slightly higher DSC of 95.62% was achieved for 3D UNet + +. Both models performed well on the Liver-CT dataset, with 3D UNet acquiring a DSC score of 95.67% and 3D UNet + + a DSC score of 97.36%. And in the case of the BTCV dataset, both models had DSC values ranging from 93.42 to 95.31%. These results demonstrate the robustness and efficiency of the models presented for clinical applications and medical research in multi-organ segmentation. This study validates the proposed architectures, underpinning and accentuating accuracy in medical imaging, creating avenues for scalable solutions for complex abdominal-imaging tasks. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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