ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024
Automation of Wilms' tumor segmentation by artificial intelligence.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Personalized Medicine in Pediatric Urology: From Diagnosis to Individualized Risk Assessment.Medical sciences (Basel, Switzerland) · 2026Review
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Classification and Segmentation of Medical Images Using Cross-Representation Attention Fusion and Fuzzy Image Enhancement.Sensors (Basel, Switzerland) · 2026Article
- AI in pediatric surgery: a narrative review.Translational pediatrics · 2026Review
- Localization-guided segmentation of Gliomas via 3D ReFusionNet.Frontiers in oncology · 2026Article
- Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.BMC medical imaging · 2025Article
- Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.Tomography (Ann Arbor, Mich.) · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
background3D reconstruction of Wilms' tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate the segmentation of tumors and kidneys in children.
methodsA manual segmentation was carried out by two experts on 14 CT scans. Then, the segmentation of Wilms' tumor and neoplastic kidney was automatically performed using the CNN U-Net and the same CNN U-Net trained according to the OV
resultsWhen segmentations were performed manually by two experts, the inter-individual variability resulted in a Dice index of 0.95 for tumor and 0.87 for kidney. Fully automatic segmentation with the CNN U-Net yielded a poor Dice index of 0.69 for Wilms' tumor and 0.27 for kidney. With the OV
conclusionFully automated segmentation remains a challenge in the field of medical image processing. Although it is possible to use already developed neural networks, such as U-Net, we found that the results obtained were not satisfactory for segmentation of neoplastic kidneys or Wilms' tumors in children. We developed an innovative CNN U-Net training method that makes it possible to segment the kidney and its tumor with the same precision as an expert while reducing their intervention time by 80%.
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