ArticleIEEE transactions on medical imaging2019
Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- Topological data analysis visualization for interpretable assessment of AI contouring quality.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- LNMSNet: a multi-task deep learning network for pulmonary nodules segmentation and malignancy classification.Frontiers in medicine · 2026Article
- Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients.Physics and imaging in radiation oncology · 2026Article
- Enriched lung cancer classification approach using an optimized hybrid deep learning approach.Scientific reports · 2025Article
- Prospective validation of a pretreatment 18F-FDG PET/CT and mean lung dose model for early radiation pneumonitis.Physics and imaging in radiation oncology · 2025Article
- Advancing deep learning-based segmentation for multiple lung cancer lesions in real-world multicenter CT scans.European radiology experimental · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Unsupervised non-small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity-based discriminator.Journal of applied clinical medical physics · 2025Article
- Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis.Current oncology (Toronto, Ont.) · 2025Article
- Anatomy guided modality fusion for cancer segmentation in PET CT volumes and images.Scientific reports · 2025Article
- Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Self-supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences.Medical physics · 2025Article
- Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer.Medical physics · 2025Article
- Leveraging multi-scale feature integration in UNet and FPN for semantic segmentation of lung nodules.Frontiers in artificial intelligence · 2025Article
- Article
- Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method.PloS one · 2025Article
- Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation.European radiology · 2024Article
- Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network.Translational cancer research · 2024Article
12 more citing papers are in PubMed but not listed here.
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8 authors.
Funding
Abstract
Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume changes from computed tomography images are essential for monitoring tumor response to therapy. Hence, we developed two multiple resolution residually connected network (MRRN) formulations called incremental-MRRN and dense-MRRN. Our networks simultaneously combine features across multiple image resolution and feature levels through residual connections to detect and segment the lung tumors. We evaluated our method on a total of 1210 non-small cell (NSCLC) lung tumors and nodules from three data sets consisting of 377 tumors from the open-source Cancer Imaging Archive (TCIA), 304 advanced stage NSCLC treated with anti- PD-1 checkpoint immunotherapy from internal institution MSKCC data set, and 529 lung nodules from the Lung Image Database Consortium (LIDC). The algorithm was trained using 377 tumors from the TCIA data set and validated on the MSKCC and tested on LIDC data sets. The segmentation accuracy compared to expert delineations was evaluated by computing the dice similarity coefficient, Hausdorff distances, sensitivity, and precision metrics. Our best performing incremental-MRRN method produced the highest DSC of 0.74 ± 0.13 for TCIA, 0.75±0.12 for MSKCC, and 0.68±0.23 for the LIDC data sets. There was no significant difference in the estimations of volumetric tumor changes computed using the incremental-MRRN method compared with the expert segmentation. In summary, we have developed a multi-scale CNN approach for volumetrically segmenting lung tumors which enables accurate, automated identification of and serial measurement of tumor volumes in the lung.
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