ArticleScientific reports2020
Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.Abdominal radiology (New York) · 2026Review
- Frugal Learning Methods for Kidney Segmentation in Non-Contrast MRI.Journal of clinical medicine · 2026Article
- Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study.Abdominal radiology (New York) · 2026Article
- Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography.Scientific reports · 2025Article
- ARViS: a bleed-free multi-site automated injection robot for accurate, fast, and dense delivery of virus to mouse and marmoset cerebral cortex.Nature communications · 2024Article
- Automation of Wilms' tumor segmentation by artificial intelligence.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation.Frontiers in radiology · 2023Article
- Fully automated condyle segmentation using 3D convolutional neural networks.Scientific reports · 2022Article
- Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging.Journal of Korean medical science · 2022Article
- Kidney Tumor Segmentation Based on FR2PAttU-Net Model.Frontiers in oncology · 2022Article
- Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Article
- Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network-Corrected Labeling in Magnetic Resonance Imaging-Derived Proton Density Fat Fraction.Journal of digital imaging · 2021Article
- Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.European radiology · 2021Article
- Deep Active Learning for Automatic Segmentation of Maxillary Sinus Lesions Using a Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2021Article
- Article
- Artificial intelligence and machine learning for medical imaging: A technology review.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.Scientific reports · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Segmentation is fundamental to medical image analysis. Recent advances in fully convolutional networks has enabled automatic segmentation; however, high labeling efforts and difficulty in acquiring sufficient and high-quality training data is still a challenge. In this study, a cascaded 3D U-Net with active learning to increase training efficiency with exceedingly limited data and reduce labeling efforts is proposed. Abdominal computed tomography images of 50 kidneys were used for training. In stage I, 20 kidneys with renal cell carcinoma and four substructures were used for training by manually labelling ground truths. In stage II, 20 kidneys from the previous stage and 20 newly added kidneys were used with convolutional neural net (CNN)-corrected labelling for the newly added data. Similarly, in stage III, 50 kidneys were used. The Dice similarity coefficient was increased with the completion of each stage, and shows superior performance when compared with a recent segmentation network based on 3D U-Net. The labeling time for CNN-corrected segmentation was reduced by more than half compared to that in manual segmentation. Active learning was therefore concluded to be capable of reducing labeling efforts through CNN-corrected segmentation and increase training efficiency by iterative learning with limited data.
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