Abdolali Fatemeh, Zoroofi Reza Aghaeizadeh, Otake Yoshito, Sato Yoshinobu
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan.
Comput Biol Med. 2016 May 1;72:108-19. doi: 10.1016/j.compbiomed.2016.03.014. Epub 2016 Mar 24.
Accurate segmentation of cysts and tumors is an essential step for diagnosis, monitoring and planning therapeutic intervention. This task is usually done manually, however manual identification and segmentation is tedious. In this paper, an automatic method based on asymmetry analysis is proposed which is general enough to segment various types of jaw cysts. The key observation underlying this approach is that normal head and face structure is roughly symmetric with respect to midsagittal plane: the left part and the right part can be divided equally by an axis of symmetry. Cysts and tumors typically disturb this symmetry. The proposed approach consists of three main steps as follows: At first, diffusion filtering is used for preprocessing and symmetric axis is detected. Then, each image is divided into two parts. In the second stage, free form deformation (FFD) is used to correct slight displacement of corresponding pixels of the left part and a reflected copy of the right part. In the final stage, intensity differences are analyzed and a number of constraints are enforced to remove false positive regions. The proposed method has been validated on 97 Cone Beam Computed Tomography (CBCT) sets containing various jaw cysts which were collected from various image acquisition centers. Validation is performed using three similarity indicators (Jaccard index, Dice's coefficient and Hausdorff distance). The mean Dice's coefficient of 0.83, 0.87 and 0.80 is achieved for Radicular, Dentigerous and KCOT classes, respectively. For most of the experiments done, we achieved high true positive (TP). This means that a large number of cyst pixels are correctly classified. Quantitative results of automatic segmentation show that the proposed method is more effective than one of the recent methods in the literature.
囊肿和肿瘤的准确分割是诊断、监测和规划治疗干预的关键步骤。这项任务通常是手动完成的，然而手动识别和分割很繁琐。本文提出了一种基于不对称分析的自动方法，该方法具有足够的通用性，可用于分割各种类型的颌骨囊肿。这种方法的关键观察结果是，正常的头部和面部结构相对于正中矢状面大致对称：左半部分和右半部分可以由对称轴平均分割。囊肿和肿瘤通常会破坏这种对称性。所提出的方法包括以下三个主要步骤：首先，使用扩散滤波进行预处理并检测对称轴。然后，将每个图像分成两部分。在第二阶段，使用自由形式变形（FFD）来校正左半部分与右半部分的反射副本的对应像素的轻微位移。在最后阶段，分析强度差异并实施一些约束以去除假阳性区域。所提出的方法已在从各个图像采集中心收集的97组包含各种颌骨囊肿的锥形束计算机断层扫描（CBCT）上得到验证。使用三个相似性指标（杰卡德指数、戴斯系数和豪斯多夫距离）进行验证。对于根端囊肿、含牙囊肿和牙源性角化囊性瘤类别，平均戴斯系数分别达到0.83、0.87和0.80。对于大多数已完成的实验，我们获得了较高的真阳性（TP）。这意味着大量的囊肿像素被正确分类。自动分割的定量结果表明，所提出的方法比文献中最近的一种方法更有效。
