ArticleJournal of applied clinical medical physics2023
Hepatic vessels segmentation using deep learning and preprocessing enhancement.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis : Neural Network-based Methods for Liver Semantic Segmentation.Journal of medical systems · 2024Pooled it
- Argus-Net: Anatomy-Guided Hierarchical Learning for Pulmonary Vasculature Segmentation in Contrast and Non-Contrast CT.Journal of imaging informatics in medicine · 2026Article
- Automated Optic Disc Tilt Classification in Fundus Photographs Using Segmentation and the Elliptical Ratio: External Clinical Validation Study.JMIR formative research · 2026Article
- Ischemic Stroke Lesion Core Segmentation from CT Perfusion Scans Using Attention ResUnet Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Improved segmentation of hepatic vascular networks in ultrasound volumes using 3D U-Net with intensity transformation-based data augmentation.Medical & biological engineering & computing · 2025Article
- 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
- Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and Vesselness.Bioengineering (Basel, Switzerland) · 2024Article
- Hepatic vessels segmentation using deep learning and preprocessing enhancement.Journal of applied clinical medical physics · 2023Article
- Progress and clinical translation in hepatocellular carcinoma of deep learning in hepatic vascular segmentation.Digital healthReview
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLiver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment.
methodsRecently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied.
resultsThe proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%.
conclusionsThe proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.
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