ArticleComputational and mathematical methods in medicine2022
Radiomics Model Based on Enhanced Gradient Level Set Segmentation Algorithm to Predict the Prognosis of Endoscopic Treatment of Sinusitis.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.American journal of rhinology & allergy · 2025Pooled it
- Deep learning radiomics-based preoperative prediction of recurrence in chronic rhinosinusitis.iScience · 2023Article
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2 authors.
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Abstract
Methods: Computed tomography (CT) images of sinusitis in 91 patients were collected. By introducing boundary gradient information into the edge detection function, the sensitivity of the level set model to the boundary of different intensities of lesions was adjusted to obtain accurate segmentation results. After that, the segmented CT image was imported into Mazda texture analysis software for feature extraction. Three dimensionality reduction methods were used to screen the best texture features. Four analysis methods in the B11 module were used to calculate the misclassified rate (MCR). Results: The segmentation algorithm based on an enhanced gradient level set has good segmentation results for sinusitis lesions. The radiomics results show that the raw data analysis method under the Fisher dimensionality reduction method has a low MCR (25.27%). Conclusion: The enhanced gradient level set segmentation algorithm can segment sinusitis lesions accurately. The radiomics model effectively predicts the prognosis of endoscopic treatment of sinusitis.
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