Okada Kazunori, Rysavy Steven, Flores Arturo, Linguraru Marius George
Department of Computer Science, San Francisco State University, San Francisco, California 94132.
Biomedical and Health Informatics Program, University of Washington, Seattle, Washington 98195.
Med Phys. 2015 Apr;42(4):1653-65. doi: 10.1118/1.4914418.
This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions.
The proposed semiautomatic solution combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost.
A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon's state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts.
Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon's conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions.
本文提出了计算机辅助诊断(CAD)在日常临床牙科挑战中的一种新应用:对根尖囊肿和肉芽肿之间的根尖周病变进行无创鉴别诊断。组织活检是目前可用于这种鉴别诊断的最可靠方法;然而,这种侵入性操作会阻碍病变的无创愈合,尽管有报告称它们可能无需手术治疗即可愈合。使用锥形束计算机断层扫描(CBCT)的CAD提供了一种替代性的无创诊断工具,有助于避免潜在的不必要手术,并研究病变未知的愈合过程和愈合速度。
所提出的半自动解决方案将基于图的随机游走分割与基于机器学习的增强分类器相结合,并提供了一种用户交互最少的强大临床工具。作为此CAD框架的一部分,作者提供了两项新颖的技术贡献:(1)通过似然比检验对随机游走分割进行概率扩展;(2)LDA-AdaBoost:加权线性判别分析与AdaBoost的新集成。
使用包含28次CBCT扫描的数据集来验证该方法,并将其与其他流行的分割和分类方法进行比较。结果表明,所提出方法的有效性达到了94.1%的正确分类率,与西蒙的最先进方法相比,性能提高了17.6%。作者还将分类性能与牙髓病专家提供的组织病理学和CBCT诊断的两个独立真值集进行了比较。
作者的实验结果表明,所提出的CAD系统与CBCT真值的一致性比与组织病理学的一致性更明显,这支持了西蒙的推测,即CBCT诊断在鉴别根尖周病变方面可以与组织病理学一样准确。