ArticleMedical physics2019
Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- A Meta-Analysis of Deep-Learning-Based Fusion of Positron Emission Tomography and Computed Tomography in Oncologic Imaging.Journal of imaging informatics in medicine · 2026Review
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk.Physics and imaging in radiation oncology · 2025Review
- Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2024Article
- Multi-modal co-learning with attention mechanism for head and neck tumor segmentation onEJNMMI physics · 2024Article
- Semisupervised 3D segmentation of pancreatic tumors in positron emission tomography/computed tomography images using a mutual information minimization and cross-fusion strategy.Quantitative imaging in medicine and surgery · 2024Article
- Segmentation model of soft tissue sarcoma based on self-supervised learning.Frontiers in oncology · 2024Article
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning model fusion improves lung tumor segmentation accuracy across variable training-to-test dataset ratios.Physical and engineering sciences in medicine · 2023Article
- A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT.Frontiers in oncology · 2023Article
- OSC-COFrontiers in plant science · 2023Article
- Detection and localization of hyperfunctioning parathyroid glands on [Radiology and oncology · 2022Article
- Deep Learning Techniques to Diagnose Lung Cancer.Cancers · 2022Review
- Deep learning in structural and functional lung image analysis.The British journal of radiology · 2022Review
- Multi-scale Selection and Multi-channel Fusion Model for Pancreas Segmentation Using Adversarial Deep Convolutional Nets.Journal of digital imaging · 2022Article
- Review
- Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.The British journal of radiology · 2021Article
- Applications of artificial intelligence in oncologicAnnals of translational medicine · 2021Review
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7 authors.
Funding
Abstract
purposeTo investigate the use and efficiency of 3-D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual-modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)-computed tomography (CT) images.
methodsWe used DFCN cosegmentation for NSCLC tumors in PET-CT images, considering both the CT and PET information. The proposed DFCN-based cosegmentation method consists of two coupled three-dimensional (3D)-UNets with an encoder-decoder architecture, which can communicate with the other in order to share complementary information between PET and CT. The weighted average sensitivity and positive predictive values denoted as Scores, dice similarity coefficients (DSCs), and the average symmetric surface distances were used to assess the performance of the proposed approach on 60 pairs of PET/CTs. A Simultaneous Truth and Performance Level Estimation Algorithm (STAPLE) of 3 expert physicians' delineations were used as a reference. The proposed DFCN framework was compared to 3 graph-based cosegmentation methods.
resultsStrong agreement was observed when using the STAPLE references for the proposed DFCN cosegmentation on the PET-CT images. The average DSCs on CT and PET are 0.861 ± 0.037 and 0.828 ± 0.087, respectively, using DFCN, compared to 0.638 ± 0.165 and 0.643 ± 0.141, respectively, when using the graph-based cosegmentation method. The proposed DFCN cosegmentation using both PET and CT also outperforms the deep learning method using either PET or CT alone.
conclusionsThe proposed DFCN cosegmentation is able to outperform existing graph-based segmentation methods. The proposed DFCN cosegmentation shows promise for further integration with quantitative multimodality imaging tools in clinical trials.
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