ArticleiScience2023
Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.Liver research (Beijing, China) · 2026Review
- Federated orthogonal learning for detection of liver lesions from multi-phase contrast-enhanced CT images.NPJ digital medicine · 2026Article
- Advances in Artificial Intelligence-Based Liver-Related Semantic Segmentation Techniques and Applications Using CT Imaging.Cancer medicine · 2026Review
- Reducing manual workload in CT and MRI annotation with the Segment Anything Model 2.BMC medical imaging · 2026Article
- Deep Learning-Based Diagnosis of Lumbar Spondylolisthesis Using X-Ray Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a fusion model based on multi-phase contrast CT radiomics combined with clinical features for predicting Ki-67 expression in gastric cancer.Biomedical reports · 2025Article
- Article
- Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.
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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.