ArticleInsights into imaging2022
Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Fully automated multi-sequence detection and alignment of focal liver lesions in dynamic contrast-enhanced MRI.European radiology · 2026Article
- [Estimation of the parenchymal reserve-Volumetric and functional before resection].Chirurgie (Heidelberg, Germany) · 2026Review
- Use of Patient-Specific 3D Models in Paediatric Surgery: Effect on Communication and Surgical Management.Journal of imaging · 2026Article
- Liver Segment and Lesion Segmentation on CT and MRI: An Open-Source Contribution to TotalSegmentator.Journal of imaging informatics in medicine · 2025Article
- Identification of abdominal MRI features associated with histopathological severity and treatment response in autoimmune hepatitis.European radiology · 2025Article
- A two-step automatic identification of contrast phases for abdominal CT images based on residual networks.Insights into imaging · 2025Article
- Comparison of automated with manual 3D qEASL assessment based on MR imaging in hepatocellular carcinoma treated with conventional TACE.Abdominal radiology (New York) · 2025Article
- AI-Based Quantification of Enhancing Tumor Volume on Contrast-Enhanced MRI to Predict Pathologic Response and Prognosis in HCC After HAIC Plus Targeted Therapy and Immunotherapy.Journal of hepatocellular carcinoma · 2025Article
- Effective Tumor Annotation for Automated Diagnosis of Liver Cancer.IEEE journal of translational engineering in health and medicine · 2025Article
- Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.European radiology · 2025Article
- Development of fully automated models for staging liver fibrosis using non-contrast MRI and artificial intelligence: a retrospective multicenter study.EClinicalMedicine · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation.IEEE journal of translational engineering in health and medicine · 2024Article
- Article
- Segmentation of Portal Vein in Multiphase CTA Image Based on Unsupervised Domain Transfer and Pseudo Label.Diagnostics (Basel, Switzerland) · 2023Article
- Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images.Cancers · 2023Article
- Segmentation of Pancreatic Subregions in Computed Tomography Images.Journal of imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectiveWe aim to develop and validate a three-dimensional convolutional neural network (3D-CNN) model for automatic liver segment segmentation on MRI images.
methodsThis retrospective study evaluated an automated method using a deep neural network that was trained, validated, and tested with 367, 157, and 158 portal venous phase MR images, respectively. The Dice similarity coefficient (DSC), mean surface distance (MSD), Hausdorff distance (HD), and volume ratio (RV) were used to quantitatively measure the accuracy of segmentation. The time consumed for model and manual segmentation was also compared. In addition, the model was applied to 100 consecutive cases from real clinical scenario for a qualitative evaluation and indirect evaluation.
resultsIn quantitative evaluation, the model achieved high accuracy for DSC, MSD, HD and RV (0.920, 3.34, 3.61 and 1.01, respectively). Compared to manual segmentation, the automated method reduced the segmentation time from 26 min to 8 s. In qualitative evaluation, the segmentation quality was rated as good in 79% of the cases, moderate in 15% and poor in 6%. In indirect evaluation, 93.4% (99/106) of lesions could be assigned to the correct segment by only referring to the results from automated segmentation.
conclusionThe proposed model may serve as an effective tool for automated anatomical region annotation of the liver on MRI images.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.