CT理论与应用研究2026，Vol.35Issue(5)：906-914,9.DOI:10.15953/j.ctta.2026.218
颞骨U-HRCT标准层面自动校正及效能验证
Automatic Alignment and Performance Validation of Temporal Bone Standard Planes in U-HRCT
摘要
Abstract
Objective:The present study aimed to validate an automatic standard plane alignment algorithm for temporal bone ultra-high resolution computed tomography(U-HRCT)using large-scale imaging datasets.Methods:Using lateral semicircular canal segmentation and statistical priors of angles between the reference lines and planes,the proposed algorithm solves the problem of tedious and inefficient manual post-processing during unilateral temporal bone examinations.We accordingly performed a clinical efficacy evaluation and analyzed cases of alignment failure to assess the reliability of this algorithm.A total of 3 094 patients(5 668 ears)with U-HRCT data were retrospectively enrolled.An inner ear substructure segmentation network model was used to automatically segment the lateral semicircular canal(LSC).Statistical priors of the extracted reference line-plane angles θ were adopted to identify and align the standard unilateral planes.A three-point scoring system was adopted to evaluate the automatic alignment outcomes of all 5 668 ear samples.For samples with failed alignment,further three-point scoring was conducted on the corresponding automatic LSC segmentation results,followed by an analysis of the causes of failure.Finally,the LSC automatic segmentation scores of the randomly sampled success and failure groups were compared.Results:Among the 5 668 enrolled ears,scoring results demonstrated that 69.83%(3 958 ears)achieved a score of 3(excellent),21.81%(1236 ears)scored 2(good),and 8.36%(474 ears)scored 1(failure),yielding a qualified rate(score≥2)of 91.64%.Among the 474 ears with alignment failure,none of the automatic LSC segmentations attained a score of 3(intact segmentation with well-defined borders);6.12%(29 ears)were graded as 2(essentially intact segmentation with partial marginal deviation),and the remaining 93.88%(445 ears)were graded as 1(incomplete segmentation,blurred boundaries,or positional offset).There was a statistically significant difference in LSC segmentation scores between the qualified and failed alignment groups,indicating that poor LSC segmentation is the predominant direct contributor to alignment failure.Etiological analysis of failures revealed the following constituent ratios:congenital inner ear anatomical variation or malformation sparing the lateral semicircular canal(10.34%),acquired abnormal temporal bone mineral density or osseous defects unrelated to LSC(27.00%),postoperative changes secondary to metallic implant placement(12.66%),and in addition to the above diseases,intrinsic algorithmic failure of automatic segmentation(50.00%).Conclusion:Overall,the proposed automatic alignment method,relying on LSC segmentation and statistical priors of reference line-plane angles,achieved a qualified rate of 91.64%for standard slice alignment of the unilateral temporal bone on U-HRCT,which markedly improved the standardization and efficiency of radiological post-processing.Alignment failure is strongly correlated with LSC segmentation quality,providing a clear direction for the optimization of subsequent algorithms.关键词
超高分辨力CT/外半规管/自动校正/自动分割Key words
ultra-high resolution CT/lateral semicircular canal/automatic alignment/automatic segmentation分类
医药卫生引用本文复制引用
李新月,杨雪,杨德武,张晔,冯懿俐,张景茜,陆文凯,李晓光,尹红霞..颞骨U-HRCT标准层面自动校正及效能验证[J].CT理论与应用研究,2026,35(5):906-914,9.基金项目
北京市科技计划项目(耳鼻喉双源锥形束计算机体层摄影设备示范应用(Z241100009024020)) （耳鼻喉双源锥形束计算机体层摄影设备示范应用(Z241100009024020）
北京市自然科学基金项目(基于U-HRCT的耳科手术面神经损伤风险智能预警关键技术研究(7252281)) （基于U-HRCT的耳科手术面神经损伤风险智能预警关键技术研究(7252281）
国家自然科学基金面上项目(基于医学知识介导的中耳病变超高分辨力CT智能判读关键技术研究(62371316)). （基于医学知识介导的中耳病变超高分辨力CT智能判读关键技术研究(62371316）
