ArticleMedical physics2021
Feature-based automated segmentation of ablation zones by fuzzy c-mean clustering during low-dose computed tomography.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Recent advances in computerized imaging and its vital roles in liver disease diagnosis, preoperative planning, and interventional liver surgery: A review.World journal of gastrointestinal surgery · 2023Review
- Article
- Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data.PloS one · 2023Article
- Advanced CT techniques for hepatic microwave ablation zone monitoring and follow-up.Abdominal radiology (New York) · 2022Article
- Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis.Computational intelligence and neuroscience · 2022Article
- Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.European journal of nuclear medicine and molecular imaging · 2021Article
- Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy.Diagnostics (Basel, Switzerland) · 2021Article
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4 authors.
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Abstract
purposeIntra-procedural monitoring and post-procedural follow-up is necessary for a successful ablation treatment. An imaging technique which can assess the ablation geometry accurately is beneficial to monitor and evaluate treatment. In this study, we developed an automated ablation segmentation technique for serial low-dose, noisy ablation computed tomography (CT) or contrast-enhanced CT (CECT).
methodsLow-dose, noisy temporal CT and CECT volumes were acquired during microwave ablation on normal porcine liver (four with non-contrast CT and eight with CECT). Highly constrained backprojection (HYPR) processing was used to recover ablation zone information compromised by low-dose noise. First-order statistic features and normalized fractional Brownian features (NBF) were used to segment ablation zones by fuzzy c-mean clustering. After clustering, the segmented ablation zone was refined by cyclic morphological processing. Automatic and manual segmentations were compared to gross pathology with Dice's coefficient (morphological similarity), while cross-sectional dimensions were compared by percent difference.
resultsAutomatic and manual segmentations of the ablation zone were very similar to gross pathology (Dice Coefficients: Auto.-Path. = 0.84 ± 0.02; Manu.-Path. = 0.76 ± 0.03, P = 0.11). The differences in ablation area, major diameter and minor diameter were 17.9 ± 3.2%, 11.1 ± 3.2% and 16.2 ± 3.4%, respectively, when comparing automatic segmentation to gross pathology, which were lower than the differences of 32.9 ± 16.8%, 13.0 ± 9.8% and 21.8 ± 5.8% when comparing manual segmentation to gross pathology. Manual segmentations tended to overestimate gross pathology when ablation area was less than 15 cm
conclusionFuzzy c-means clustering may be used to aid automatic segmentation of ablation zones without prior information or user input, making serial CT/CECT has more potential to assess treatments intra-procedurally.
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