ArticleAnnals of surgery open : perspectives of surgical history, education, and clinical approaches2022
nnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images.
Article in Annals of surgery open : perspectives of surgical history, education, and clinical approaches, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The trial behind it
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Who cites it
20 citing papers in PubMed.
- Charge Au@Pt NPs combined with 3D STS-Net for adaptive and sensitized radiotherapy of hepatocellular carcinoma: Synergistic enhancement of therapeutic gain across physical and biological dimensions.Bioactive materials · 2026Article
- Osegnet-F-Unext: O-Segnet-Fusion-Unext for pulmonary lobe segmentation of Covid-19 using Computed Tomography image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Comparison of Manual, Semi-Automatic, and Automatic CT-Based Methods for Liver Volume Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning enables fully automated cineCT-based assessment of regional right ventricular function.European heart journal. Imaging methods and practice · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Precision surgery in the era of 3D visualization, AR/VR, and 3D printing: current applications and future directions.Frontiers in medicine · 2026Review
- The Value of Chest and Abdominal CT Scans in Deceased Organ Donor Screening: A Prospective Nationwide Cohort Study.Transplantation direct · 2025Article
- AI-Based 3D Liver Segmentation and Volumetric Analysis in Living Donor Data.Journal of imaging informatics in medicine · 2025Article
- Deep learning for automatic segmentation of hepatocellular carcinoma in contrast enhanced CT scans.Scientific reports · 2025Article
- Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Assessing Quantitative Performance and Expert Review of Multiple Deep Learning-Based Frameworks for Computed Tomography-based Abdominal Organ Auto-Segmentation.Intelligent oncology · 2025Article
- Necessity and impact of specialization of large foundation model for medical segmentation tasks.Medical physics · 2025Article
- Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma.Nano letters · 2024Article
- Dual Attention-Based 3D U-Net Liver Segmentation Algorithm on CT Images.Bioengineering (Basel, Switzerland) · 2024Article
- External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.European radiology · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy.Frontiers in oncology · 2024Article
- Esophageal cancer detection via non-contrast CT and deep learning.Frontiers in medicine · 2024Article
- En-DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis.Biomedicines · 2023Article
- Automated Evans index measurement using deep learning in acute subarachnoid hemorrhage: reliability, agreement with experts, and association with external ventricular drainage.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Background: Recipient donor matching in liver transplantation can require precise estimations of liver volume. Currently utilized demographic-based organ volume estimates are imprecise and nonspecific. Manual image organ annotation from medical imaging is effective; however, this process is cumbersome, often taking an undesirable length of time to complete. Additionally, manual organ segmentation and volume measurement incurs additional direct costs to payers for either a clinician or trained technician to complete. Deep learning-based image automatic segmentation tools are well positioned to address this clinical need. Objectives: To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical images. Methods: We trained a nnU-Net deep learning model to identify liver borders in images of the abdominal cavity. We used 151 publicly available CT scans. For each CT scan, a board-certified radiologist annotated the liver margins (ground truth annotations). We split our image dataset into training, validation, and test sets. We trained our nnU-Net model on these data to identify liver borders in 3D voxels and integrated these to reconstruct a total organ volume estimate. Results: The nnU-Net model accurately identified the border of the liver with a mean overlap accuracy of 97.5% compared with ground truth annotations. Our calculated volume estimates achieved a mean percent error of 1.92% + 1.54% on the test set. Conclusions: Precise volume estimation of livers from CT scans is accurate using a nnU-Net deep learning architecture. Appropriately deployed, a nnU-Net algorithm is accurate and quick, making it suitable for incorporation into the pretransplant clinical decision-making workflow.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.