ReviewJournal of digital imaging2020
Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters.
Review in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Who cites it
13 citing papers in PubMed.
- User-preference alignment with uncertainty-aware interactive rectification for liver organ and tumor segmentation and analysis from CT images.NPJ digital medicine · 2026Article
- Foundation model-guided multi-view semi-supervised CT segmentation of liver tumors in resource-constrained settings.NPJ digital medicine · 2025Article
- Towards a zero-shot low-latency navigation for open surgery augmented reality applications.International journal of computer assisted radiology and surgery · 2025Article
- Anatomically Guided Cascaded U-Net Ensemble for Coronary Artery Calcification Segmentation in Cardiac CT.Bioengineering (Basel, Switzerland) · 2025Article
- A Novel Attention-based Network for Geometry Reconstruction with Error Estimation from Medical Images.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- A Review of Advancements and Challenges in Liver Segmentation.Journal of imaging · 2024Review
- 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
- RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Deep learning and level set approach for liver and tumor segmentation from CT scans.Journal of applied clinical medical physics · 2020Article
- Local, semi-automatic, three-dimensional liver reconstruction or external provider? An analysis of performance and time expense.Langenbeck's archives of surgery · 2020Article
- Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning.International journal of computer assisted radiology and surgery · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.
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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.