System for Automatic Segmentation of Male Pelvis Structures from CT Images

描述（由申请人提供）：图像分割是一种常用的临床实践，用于从体积图像（如计算机断层扫描和磁共振图像）中提取内部解剖对象的几何描述。当前实践的不足激发了一个重要的研究领域，涉及用于自动分割的统计可训练变形形状模型（DSM）。一般方法是将DSM应用于图像，并使模型经历一系列变形，这些变形收敛到模型与目标解剖对象之间的紧密匹配。在适当的统计框架中，通过数学优化来驱动变形。总体目标是开发和临床评估一种工作站，用于从CT图像中自动分割男性骨盆中的解剖结构，用于使用基于称为m-reps. Segmented CT图像的特别强大的DSM类技术的放射治疗中的图像引导应用。很可能，与所有其他医学应用组合相比，更经常地为这些目的执行分割。目前临床实践中的交互式轮廓绘制方法非常耗时且昂贵，并且轮廓显示出显著的用户间和用户内变化，这对依赖于它们的临床决策产生不利影响。所提出的方法将克服这些缺点，提高放射治疗治疗癌症的有效性。

DESCRIPTION (provided by applicant): Image segmentation is a commonly performed clinical practice for extracting geometrical descriptions of internal anatomical objects from volume images such as computed tomography and magnetic resonance images. Shortcomings of current practice have motivated an important area of research involving statistically trainable deformable-shape models (DSMs) for automatic segmentation. The general approach is to apply a DSM to an image and cause the model to undergo a series of deformations converging to a close match between the model and the target anatomical object(s). The deformations are driven, in an appropriate statistical framework, by mathematical optimization. The overall aim is to develop and clinically evaluate a workstation for automatic segmentation of anatomical structures in the male pelvis from CT images for image- guided applications in radiation therapy using technology based on a particularly powerful class of DSMs called m-reps. Segmented CT images provide guidance for critical treatment planning and radiation delivery decisions in radiation therapy. It is likely that segmentation is performed more often for these purposes than for all the other medical applications combined. Current interactive contouring methods in clinical practice are extremely time consuming and expensive, and the contours demonstrate significant inter- and intra-user variabilities that adversely affect the clinical decisions that rely on them. The proposed methodology will overcome these shortcomings and improve the effectiveness of radiation therapy for treating cancer.