Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
Med Phys. 2012 Sep;39(9):5419-28. doi: 10.1118/1.4742845.
The purpose of this work was to develop and validate a computer-aided method for the 3D segmentation of lymph nodes in CT images. The proposed method can be utilized to facilitate applications like biopsy planning, image guided radiation treatment, or assessment of response to therapy.
An optimal surface finding based lymph node segmentation method was developed. Based on the approximate center point of a lymph node of interest, a graph is generated, which represents the local neighborhood around the lymph node at discrete locations (graph nodes). A cost function is calculated based on a weighted edge and region homogeneity term. By means of optimization, a surface-based segmentation of the lymph node is derived. In addition, an interactive segmentation refinement algorithm was developed, which allows the user to quickly correct segmentation errors, if needed. For assessment of segmentation accuracy, 111 lymph nodes of mediastinum, abdomen, head/neck, and axillary regions from 35 volumetric CT scans were utilized. For accuracy analysis, lymph nodes were divided into three test sets based on lymph node size and spatial resolution of the CT scan. The average lymph node size for test set I, II, and III was 1056, 1621, and 501 mm(3), respectively. Spatial resolution of test set II was lower than for test sets I and III. To generate an independent reference standard for comparison, all 111 lymph nodes were segmented by an expert with a live wire approach.
All test sets were segmented with the proposed approach. Out of the 111 lymph nodes, 40 cases (36%) required computer-aided refinement of initial segmentation results. The refinement typically required 10 s per lymph node. The mean and standard deviation of the Dice coefficient for final segmentations was 0.847 ± 0.061, 0.836 ± 0.058, and 0.809 ± 0.070 for test sets I, II, and II, respectively. The average signed surface distance error was 0.023 ± 0.171, 0.394 ± 0.189, and 0.001 ± 0.146 mm for test sets I, II, and II, respectively. The time required for locating the approximate center point of a target lymph node in a scan, generating an initial OSF segmentation, and refining the segmentation, if needed, is typically less than one minute.
Segmentation of lymph nodes in volumetric CT images is a challenging task due to partial volume effects, nearby strong edges, neighboring structures with similar intensity profiles and potentially inhomogeneous density of lymph nodes. The presented approach addresses many of these obstacles. In the majority of cases investigated, the initial segmentation method delivered results that did not require further processing. In addition, the computer-aided segmentation refinement framework was found to be effective in dealing with potentially occurring segmentation errors.
本研究旨在开发并验证一种用于 CT 图像中淋巴结三维分割的计算机辅助方法。该方法可用于辅助活检规划、图像引导放射治疗或治疗反应评估等应用。
我们开发了一种基于最优表面的淋巴结分割方法。基于感兴趣淋巴结的近似中心点，生成一个表示淋巴结在离散位置（图节点）处局部邻域的图。基于加权边和区域同质性项计算代价函数。通过优化，得出基于表面的淋巴结分割。此外，还开发了一种交互式分割细化算法，如果需要，允许用户快速纠正分割错误。为了评估分割准确性，我们使用了来自 35 例容积 CT 扫描的 111 个纵隔、腹部、头颈部和腋窝区域的淋巴结。为了进行准确性分析，根据淋巴结大小和 CT 扫描的空间分辨率，将淋巴结分为三组测试集。测试集 I、II 和 III 的平均淋巴结大小分别为 1056、1621 和 501mm³。测试集 II 的空间分辨率低于测试集 I 和 III。为了生成独立的参考标准进行比较，我们使用活线方法由一位专家对所有 111 个淋巴结进行了分割。
我们使用提出的方法对所有测试集进行了分割。在 111 个淋巴结中，有 40 个病例（36%）需要计算机辅助细化初始分割结果。每个淋巴结的细化通常需要 10 秒。最终分割的 Dice 系数平均值和标准差分别为测试集 I、II 和 III 的 0.847 ± 0.061、0.836 ± 0.058 和 0.809 ± 0.070。平均符号表面距离误差分别为测试集 I、II 和 III 的 0.023 ± 0.171、0.394 ± 0.189 和 0.001 ± 0.146mm。在扫描中定位目标淋巴结近似中心点、生成初始 OSF 分割以及如果需要细化分割所需的时间通常不到一分钟。
由于部分容积效应、附近的强边缘、具有相似强度分布的邻近结构以及淋巴结密度可能不均匀，容积 CT 图像中淋巴结的分割是一项具有挑战性的任务。所提出的方法解决了许多这些障碍。在所研究的大多数情况下，初始分割方法提供的结果不需要进一步处理。此外，还发现计算机辅助分割细化框架在处理潜在的分割错误方面非常有效。
