ArticleKorean journal of radiology2020
Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images.
Article in Korean journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.Biomedical engineering online · 2024Pooled it
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Identification of Prognostic Factors and Regulatory Pathways in Porto-Sinusoidal Vascular Disorder.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Precision of computed tomography volumetry in living donor liver transplantation: current evidence, limitations, and future directions.Hepatobiliary surgery and nutrition · 2026Article
- Organ Segmentation with Machine Learning Models.Journal of imaging · 2026Article
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- Spleen volume change as a prognostic and immunologic imaging biomarker in extensive-stage small-cell lung cancer receiving chemo-immunotherapy.Therapeutic advances in medical oncology · 2026Article
- Deep learning-based volumetry of the future liver remnants and prediction of candidates for major hepatectomy.BMC medical imaging · 2025Article
- Effects of patient and imaging factors on small bowel motility scores derived from deep learning-based segmentation of cine MRI.European radiology · 2025Article
- Association between thyroid function and acute mountain sickness upon rapid ascent to 3650 m in euthyroid lowlanders in China.Experimental physiology · 2025Article
- Diagnosis of moderate-to-severe hepatic steatosis using deep learning-based automated attenuation measurements on contrast-enhanced CT.Abdominal radiology (New York) · 2025Article
- Artificial Intelligence-based Liver Volume Measurement Using Preoperative and Postoperative CT Images.Current medical imaging · 2025Article
- CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma.European radiology · 2025Article
- [Application Value of an AI-based Imaging Feature Parameter Model for Predicting the Malignancy of Part-solid Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
- Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis.Abdominal radiology (New York) · 2025Article
- Model-Assisted Spleen Contouring for Assessing Splenomegaly in Myelofibrosis: A Fast and Reproducible Approach to Evaluate Progression and Treatment Response.Journal of clinical medicine · 2025Article
- A Review of Advancements and Challenges in Liver Segmentation.Journal of imaging · 2024Review
- Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.Journal of applied clinical medical physics · 2024Article
- The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review.Insights into imaging · 2024Review
- Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence.Korean journal of radiology · 2024Review
Corrections and comments
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Authors and funding
9 authors.
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Abstract
objectiveMeasurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions. MATERIALS AND
methodsA DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation.
resultsIn test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (
conclusionThe DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.
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