ArticleQuantitative imaging in medicine and surgery2020
An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing.
Article in Quantitative imaging in medicine and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 34 citations in OpenAlex.
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- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
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- Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.Journal of imaging informatics in medicine · 2024Article
- A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2023Article
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- Harmonization Strategies in Multicenter MRI-Based Radiomics.Journal of imaging · 2022Review
- Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model.Quantitative imaging in medicine and surgery · 2022Article
- Radiomics: a primer on high-throughput image phenotyping.Abdominal radiology (New York) · 2022Review
- Two-stage multitask U-Net construction for pulmonary nodule segmentation and malignancy risk prediction.Quantitative imaging in medicine and surgery · 2022Article
- Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT.Quantitative imaging in medicine and surgery · 2021Article
- Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From AdenocarcinomaFrontiers in oncology · 2021Article
- Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors.Frontiers in oncology · 2021Article
- PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines.Medical physics · 2020Article
- Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification.Quantitative imaging in medicine and surgery · 2020Article
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8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundNowadays, computer technology is getting popular for clinical aided diagnosis, especially in the direction of medical images. It makes physician diagnosis of lung nodules more efficient by providing them with reliable and accurate segmentation.
methodsA region growing based semi-automated pulmonary nodule segmentation algorithm (ReGANS) was developed with three improvements: an automatic threshold calculation method, a lesion area pre-projection method, and an optimized region growing method. The algorithm can quickly and accurately segment a whole lung nodule in a set of computed tomography (CT) images based on an initial manual point.
resultsThe average time taken for ReGANS to segment 1 pulmonary nodule was 0.83s, and the probability rand index (PRI), global consistency error (GCE), and variation of information (VoI) from a comparison between the algorithm and the radiologist's 2 manual results were 0.93, 0.06, and 0.3 for the boundary range (BR), and 0.86, 0.06, 0.3 for the precise range (PR). The number of images covered by one pulmonary nodule in a CT image set was also evaluated to compare the segmentation algorithm with the radiologist's results, with an error rate of 15%. At the same time, the results were verified in multiple data sets to validate the robustness.
conclusionsCompared with other algorithms, ReGANS can segment the lung nodule image region more quickly and more precisely. The experimental results show that ReGANS can assist medical imaging diagnosis and has good clinical application value. It also provides a faster and more convenient method for pre-data preparation of intelligent algorithms.
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