ArticleInternational journal of computer assisted radiology and surgery2019
Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.European radiology · 2023Pooled it
- Machine learning for predicting the risk stratification of 1-5 cm gastric gastrointestinal stromal tumors based on CT.BMC medical imaging · 2023Trial
- Federated orthogonal learning for detection of liver lesions from multi-phase contrast-enhanced CT images.NPJ digital medicine · 2026Article
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging.Abdominal radiology (New York) · 2025Article
- Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.Precision radiation oncology · 2025Article
- From CNNs to SAM: A Survey of Deep Learning Techniques for Liver Tumor Segmentation in CT Images.IEEE access : practical innovations, open solutions · 2025Article
- Harnessing the Power of AI for Enhanced Diagnosis and Treatment of Hepatocellular Carcinoma.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Review
- Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning.iScience · 2023Article
- Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks.European radiology · 2023Article
- Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework.Frontiers in genetics · 2023Review
- Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.World journal of surgical oncology · 2022Article
- RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.BME frontiers · 2022Article
- Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury.Frontiers in neuroscience · 2022Article
- Radiomics in hepatocellular carcinoma: A state-of-the-art review.World journal of gastrointestinal oncology · 2021Review
- State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2021Review
- Role of 3D Volumetric and Perfusion Imaging for Detecting Early Changes in Pancreatic Adenocarcinoma.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeWe address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach.
methodsWe devise a cascaded convolutional neural network based on the U-Net architecture. Two strategies for dealing with multiphase information are compared: Single-phase images are concatenated in a multi-dimensional features map on the input layer, or output maps are computed independently for each phase before being merged to produce the final segmentation. Each network of the cascade is specialized in the segmentation of a specific tissue. The performances of these networks taken separately and of the cascaded architecture are assessed on both single-phase and on multiphase images.
resultsIn terms of Dice coefficients, the proposed method is on par with a state-of-the-art method designed for automatic MR image segmentation and outperforms previously used technique for interactive CT image segmentation. We validate the hypothesis that several cascaded specialized networks have a higher prediction accuracy than a single network addressing all tasks simultaneously. Although the portal venous phase alone seems to provide sufficient contrast for discriminating tumors from healthy parenchyma, the multiphase information brings significant improvement for the segmentation of cancerous tissues (active versus necrotic part).
conclusionThe proposed cascaded multiphase architecture showed promising performances for the automatic segmentation of liver tissues, allowing to reliably estimate the necrosis rate, a valuable imaging biomarker of the clinical outcome.
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