三维CT图像的标记与分割

S. Banik, R. Rangayyan, G. Boag
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引用次数: 21

摘要

儿童CT图像的分割和标记对于计算机辅助诊断(CAD)、治疗计划以及对正常和病理区域的客观分析非常重要和有用。存在肿瘤的器官和组织的识别和分割是困难的。神经母细胞瘤原发肿块的自动分割有助于对肿瘤的组织组成、形状和大小进行再现性和客观的分析。然而,由于神经母细胞肿瘤的组织组成不均匀,从低衰减坏死到高衰减钙化,肿瘤肿块的分割是一个具有挑战性的问题。在这种情况下,本书描述了识别和分割几个腹部和胸部标志的方法,以帮助在儿童CT图像中分割神经母细胞肿瘤。描述了自动识别和分割周围伪影和组织、肋骨结构、脊柱、椎管、隔膜和骨盆表面的方法。通过与放射科医生独立手工分割的结果进行比较,还提出了定量评估脊柱、椎管、隔膜和骨盆带分割结果的技术。使用标记和切除几个组织和器官被证明有助于将肿瘤分割过程的范围限制在腹部,从而减少假阳性误差,并改善神经母细胞肿瘤的分割结果。目录表:医学图像分析概论/图像分割/实验设计与数据库/肋骨、脊柱和椎管/横膈膜的描绘/骨盆带的描绘/地标的应用/结束语
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Landmarking and Segmentation of 3D CT Images
Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissu s, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks
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