Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Int J Comput Assist Radiol Surg. 2009 May;4(3):245-62. doi: 10.1007/s11548-009-0289-y. Epub 2009 Feb 26.
Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis, treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors is difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and volume. 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, we explore methods for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images.
Methods are proposed to identify and segment automatically peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface. The results of segmentation of the vertebral column, the spinal canal, the diaphragm and the pelvic girdle are quantitatively evaluated 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 assisted in limiting the scope of the tumor segmentation process to the abdomen, and resulted in the reduction of the false-positive error rates by 22.4%, on the average, over ten CT exams of four patients, and improved the result of segmentation of neuroblastic tumors.
对儿科患者 CT 图像进行分割和标记，对于计算机辅助诊断、治疗计划以及对正常和病理区域的客观分析非常重要且有用。在存在肿瘤的情况下识别和分割器官和组织是困难的。神经母细胞瘤原发肿瘤的自动分割可以促进对肿瘤组织成分、形状和体积的可重复和客观分析。然而，由于神经母细胞瘤的组织成分具有异质性，从低衰减坏死到高衰减钙化，因此肿瘤肿块的分割是一个具有挑战性的问题。在这种情况下，我们探索了识别和分割几个腹部和胸部标志点的方法，以协助儿科 CT 图像中神经母细胞瘤的分割。
提出了自动识别和分割外周伪影和组织、肋骨结构、脊柱、椎管、横膈膜和骨盆表面的方法。通过与放射科医生独立进行的手动分割结果进行比较，对脊柱、椎管、横膈膜和骨盆带的分割结果进行了定量评估。
使用这些标志点和去除几个组织和器官有助于将肿瘤分割过程的范围限制在腹部，平均减少了 22.4%的假阳性错误率，在 4 名患者的 10 次 CT 检查中，并且改善了神经母细胞瘤的分割结果。
