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Feature‐based automated segmentation of ablation zones by fuzzy c‐mean clustering during low‐dose computed tomography
Author(s) -
Wu Pohung,
Bedoya Mariajose,
White Jim,
Brace Christopher L.
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14623
Subject(s) - ablation , segmentation , nuclear medicine , cluster analysis , sørensen–dice coefficient , ablation zone , artificial intelligence , computer science , medicine , pattern recognition (psychology) , image segmentation
Purpose Intra‐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). Methods Low‐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. Results Automatic 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 2 , but the automated segmentation tended to underestimate gross pathology when ablation zone is larger than 20 cm 2 . Conclusion Fuzzy 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.