ArticleQuantitative imaging in medicine and surgery2021
Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 41 citations in OpenAlex.
- Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis.BMC medical imaging · 2025Article
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
- Segmentation of Substantia Nigra in Brain Parenchyma Sonographic Images Using Deep Learning.Journal of imaging · 2023Article
- A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer.Cancers · 2023Review
- Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis.Insights into imaging · 2023Review
- Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [Sensors (Basel, Switzerland) · 2023Article
- Ability of three-dimensional 3-Tesla ultrashort echo time magnetic resonance imaging to display the morphological characteristics of pulmonary nodules: a sensitivity analysis.Quantitative imaging in medicine and surgery · 2023Article
- A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer.Quantitative imaging in medicine and surgery · 2023Article
- Qualitative and Semiquantitative Parameters ofCancers · 2023Article
- Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.Journal of clinical medicine · 2022Article
- Development and performance evaluation of a deep learning lung nodule detection system.BMC medical imaging · 2022Article
- A deep learning-based post-processing method for automated pulmonary lobe and airway trees segmentation using chest CT images in PET/CT.Quantitative imaging in medicine and surgery · 2022Article
- SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment.Quantitative imaging in medicine and surgery · 2022Article
- Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising.Quantitative imaging in medicine and surgery · 2022Article
- Article
- Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study.Diagnostics (Basel, Switzerland) · 2021Article
- A Cascaded Neural Network for Staging in Non-Small Cell Lung Cancer Using Pre-Treatment CT.Diagnostics (Basel, Switzerland) · 2021Article
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9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate segmentation of pulmonary nodules on computed tomography (CT) scans plays a crucial role in the evaluation and management of patients with suspicion of lung cancer (LC). When performed manually, not only the process requires highly skilled operators, but is also tiresome and time-consuming. To assist the physician in this task several automated and semi-automated methods have been proposed in the literature. In recent years, in particular, the appearance of deep learning has brought about major advances in the field.
methodsTwenty-four (12 conventional and 12 based on deep learning) semi-automated-'one-click'-methods for segmenting pulmonary nodules on CT were evaluated in this study. The experiments were carried out on two datasets: a proprietary one (383 images from a cohort of 111 patients) and a public one (259 images from a cohort of 100). All the patients had a positive transcript for suspect pulmonary nodules.
resultsThe methods based on deep learning clearly outperformed the conventional ones. The best performance [Sørensen-Dice coefficient (DSC)] in the two datasets was, respectively, 0.853 and 0.763 for the deep learning methods, and 0.761 and 0.704 for the traditional ones.
conclusionsDeep learning is a viable approach for semi-automated segmentation of pulmonary nodules on CT scans.
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