Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art fully automated lung segmentation methods fail to make their way into the clinical practice due to their inability to efficiently incorporate human input for handling misclassifications and praxis. This paper presents a lung annotation tool for CT images that is interactive, efficient, and robust. The proposed annotation tool produces an "as accurate as possible" initial annotation based on the fuzzy-connectedness image segmentation, followed by efficient manual fixation of the initial extraction if deemed necessary by the practitioner. To provide maximum flexibility to the users, our annotation tool is supported in three major operating systems (Windows, Linux, and the Mac OS X). The quantitative results comparing our free software with commercially available lung segmentation tools show higher degree of consistency and precision of our software with a considerable potential to enhance the performance of routine clinical tasks.
翻译:在临床环境中,从计算成色学(CT)扫描中准确和快速抽取肺部积量仍是一项巨大的临床环境需求,因为现有方法无法提供通用的解决方案,因为肺部的解剖变化和病理存在,因此无法提供通用的解决方案。人工注解(目前的黄金标准)耗时,往往带有人类偏见。另一方面,目前最先进的完全自动化的肺分解方法无法进入临床实践,原因是它们无法有效地将人的投入纳入处理错误分类和功能的处理中。本文件为CT图像提供了一个互动、高效和健全的肺部注解工具。拟议的注解工具根据模糊连接图象的分解产生“尽可能准确”的初步注解,随后,如果执业者认为有必要,则对最初提取图解析进行高效的手工固定。为了给用户提供最大的灵活性,我们的注工具在三大操作系统(温多斯、林克斯和麦克OS X)中得到支持。将我们的免费软件与商业上可获取的肺部分解的常规工具进行比较的量化结果,显示了我们相当程度的临床分解工具的准确性。