ArticleRadiation oncology (London, England)2025
Automatic cervical tumors segmentation in PET/MRI by parallel encoder U-net.
Article in Radiation oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review.Journal of medical radiation sciences · 2026Review
- Review
- Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients.Frontiers in oncology · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundAutomatic segmentation of cervical tumors is important in quantitative analysis and radiotherapy planning.
methodsA parallel encoder U-Net (PEU-Net) integrating the multi-modality information of PET/MRI was proposed to segment cervical tumor, which consisted of two parallel encoders with the same structure for PET and MR images. The features of the two modalities were extracted separately and fused at each layer of the decoder. Res2Net module on skip connection aggregated the features of various scales and refined the segmentation performance. PET/MRI images of 165 patients with cervical cancer were included in this study. U-Net, TransUNet, and nnU-Net with single or multi-modality (PET or/and T2WI) input were used for comparison. The Dice similarity coefficient (DSC) with volume data, DSC and the 95th percentile of Hausdorff distance (HD95) with tumor slices were calculated to evaluate the performance.
resultsThe proposed PEU-Net exhibited the best performance (DSC
conclusionsThe networks with multi-modality input outperformed those with single-modality images as input. The results showed that the proposed PEU-Net could use multi-modality information more effectively through the redesigned structure and achieved competitive performance.
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