ArticleInsights into imaging2021
Fully automated pelvic bone segmentation in multiparameteric MRI using a 3D convolutional neural network.
Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Clinical applications of MRI-based artificial intelligence in spinal metastases: A systematic review.Journal of bone oncology · 2026Review
- Critical dialogue: how imaging evaluates and guides pelvic exenteration surgery-a multidisciplinary perspective.Insights into imaging · 2026Article
- Automated segmentation of the fibula from CT imaging using two-stepped deep learning in 3D U-Net architectures.Scientific reports · 2025Article
- Accuracy and Reliability of Synthetic Computed Tomography for Model-Based Tracking of Biplane Videoradiography Data.Annals of biomedical engineering · 2025Article
- A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans.Nature communications · 2025Article
- Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning.Scientific reports · 2025Article
- Deep-learning-based pelvic automatic segmentation in pelvic fractures.Scientific reports · 2024Article
- Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.European radiology · 2023Article
- Analysis of interreader agreement in structured reports of pelvic multiparametric magnetic resonance imaging using the METastasis Reporting and Data System for Prostate Cancer guidelines.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- MAIC-10 brief quality checklist for publications using artificial intelligence and medical images.Insights into imaging · 2023Review
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
- Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy.Insights into imaging · 2022Article
- Detection and Segmentation of Pelvic Bones Metastases in MRI Images for Patients With Prostate Cancer Based on Deep Learning.Frontiers in oncology · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate segmentation of pelvic bones is an initial step to achieve accurate detection and localisation of pelvic bone metastases. This study presents a deep learning-based approach for automated segmentation of normal pelvic bony structures in multiparametric magnetic resonance imaging (mpMRI) using a 3D convolutional neural network (CNN).
methodsThis retrospective study included 264 pelvic mpMRI data obtained between 2018 and 2019. The manual annotations of pelvic bony structures (which included lumbar vertebra, sacrococcyx, ilium, acetabulum, femoral head, femoral neck, ischium, and pubis) on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images were used to create reference standards. A 3D U-Net CNN was employed for automatic pelvic bone segmentation. Additionally, 60 mpMRI data from 2020 were included and used to evaluate the model externally.
resultsThe CNN achieved a high Dice similarity coefficient (DSC) average in both testing (0.80 [DWI images] and 0.85 [ADC images]) and external (0.79 [DWI images] and 0.84 [ADC images]) validation sets. Pelvic bone volumes measured with manual and CNN-predicted segmentations were highly correlated (R
conclusionsA deep learning-based method can achieve automated pelvic bone segmentation on DWI and ADC images with suitable quantitative and qualitative performance.
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