ArticleJournal of applied clinical medical physics2020
Liver tumor segmentation based on 3D convolutional neural network with dual scale.
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 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 78 citations in OpenAlex.
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks onMolecular imaging and radionuclide therapy · 2026Article
- Two-Stage Automatic Liver Classification System Based on Deep Learning Approach Using CT Images.Journal of imaging informatics in medicine · 2026Article
- FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms.Journal of cancer research and clinical oncology · 2025Article
- A Comparative Study of Decoders for Liver and Tumor Segmentation Using a Self-ONN-Based Cascaded Framework.Diagnostics (Basel, Switzerland) · 2024Article
- An improved 3D-UNet-based brain hippocampus segmentation model based on MR images.BMC medical imaging · 2024Article
- Sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images.Medical & biological engineering & computing · 2024Article
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- A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
- En-DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis.Biomedicines · 2023Article
- Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver.World journal of gastroenterology · 2023Review
- When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects.iLIVER · 2023Review
- The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.Cancers · 2022Review
- Decision Support System for Liver Lesion Segmentation Based on Advanced Convolutional Neural Network Architectures.Bioengineering (Basel, Switzerland) · 2022Article
- Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.BMC medical imaging · 2022Article
- Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.Journal of applied clinical medical physics · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLiver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The contrast between the liver tumors and healthy tissues in CT images is low and the boundary is blurred; (b) The image of liver tumor is complex and diversified in size, shape, and location.
methodsTo solve the above problems, this paper focused on the human liver and liver tumor segmentation algorithm based on convolutional neural network (CNN), and specially designed a three-dimensional dual path multiscale convolutional neural network (TDP-CNN). To balance the performance of segmentation and requirement of computational resources, the dual path was used in the network, then the feature maps from both paths were fused at the end of the paths. To refine the segmentation results, we used conditional random fields (CRF) to eliminate the false segmentation points in the segmentation results to improve the accuracy.
resultsIn the experiment, we used the public dataset liver tumor segmentation (LiTS) to analyze the segmentation results qualitatively and quantitatively. Ground truth segmentation of liver and liver tumor was manually labeled by an experienced radiologist. Quantitative metrics were Dice, Hausdorff distance, and average distance. For the segmentation results of liver tumor, Dice was 0.689, Hausdorff distance was 7.69, and the average distance was 1.07; for the segmentation results of the liver, Dice was 0.965, Hausdorff distance was 29.162, and the average distance was 0.197. Compared with other liver and liver tumor segmentation algorithms in Medical Image Computing and Intervention (MICCAI) 2017 competition, our method of liver segmentation ranked first, and liver tumor segmentation ranked second.
conclusionsThe experimental results showed that the proposed algorithm had good performance in both liver and liver tumor segmentation.
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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.