ArticleJournal of applied clinical medical physics2020
Deep learning and level set approach for liver and tumor segmentation from CT scans.
Article in Journal of applied clinical medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Large Intestine Model Editor (LIME): Design and pilot evaluation of an editor for parameterized large intestine 3D models.Medical & biological engineering & computing · 2026Article
- Deep learning-based auto-segmentation and RECIST evaluation after concurrent chemoradiotherapy in locally advanced hepatocellular carcinoma patients.Frontiers in oncology · 2026Article
- Ischemic Stroke Lesion Core Segmentation from CT Perfusion Scans Using Attention ResUnet Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Anatomically Guided Cascaded U-Net Ensemble for Coronary Artery Calcification Segmentation in Cardiac CT.Bioengineering (Basel, Switzerland) · 2025Article
- Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and Vesselness.Bioengineering (Basel, Switzerland) · 2024Article
- Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma.Journal of personalized medicine · 2024Review
- MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging.Scientific reports · 2023Article
- Hepatic vessels segmentation using deep learning and preprocessing enhancement.Journal of applied clinical medical physics · 2023Article
- Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model.Biomedicines · 2023Article
- Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images.Physical and engineering sciences in medicine · 2023Article
- When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects.iLIVER · 2023Review
- Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet.Bioengineering (Basel, Switzerland) · 2023Article
- Practical utility of liver segmentation methods in clinical surgeries and interventions.BMC medical imaging · 2022Article
- Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm.Contrast media & molecular imaging · 2022Article
- Evaluation of clinical applicability of automated liver parenchyma segmentation of multi-center magnetic resonance images.European journal of radiology open · 2022Article
- Deep learning in hepatocellular carcinoma: Current status and future perspectives.World journal of hepatology · 2021Review
- Artificial intelligence in gastroenterology and hepatology: Status and challenges.World journal of gastroenterology · 2021Review
- Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures.BioMed research international · 2021Article
- Artificial Intelligence in Hepatology- Ready for the Primetime.Journal of clinical and experimental hepatologyReview
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSegmentation of liver organ and tumors from computed tomography (CT) scans is an important task for hepatic surgical planning. Manual segmentation of liver and tumors is tedious, time-consuming, and biased to the clinician experience. Therefore, automatic segmentation of liver and tumors is highly desirable. It would improve the surgical planning treatments and follow-up assessment.
methodThis work presented the development of an automatic method for liver and tumor segmentation from CT scans. The proposed method was based on fully convolutional neural (FCN) network with region-based level set function. The framework starts to segment the liver organ from CT scan, which is followed by a step to segment tumors inside the liver envelope. The fully convolutional network is trained to predict the coarse liver/tumor segmentation, while the localized region-based level aims to refine the predicted segmentation to find the correct final segmentation.
resultsThe effectiveness of the proposed method is validated against two publically available datasets, LiTS and IRCAD datasets. Dice scores for liver and tumor segmentation in IRCAD datasets are 95.2% and 76.1%, respectively, while for LiTS dataset are 95.6% and 70%, respectively.
conclusionThe proposed method succeeded to segment liver and tumors in heterogeneous CT scans from different scanners, as in IRCAD dataset, which proved its ability for generalization and be promising tool for automatic analysis of liver and its tumors in clinical routine.
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