Department of Electrical, Electronic, Telecommunications Eng. and Naval Architecture (DITEN), Via Opera Pia 11a, Genova, 16145, Università degli Studi di Genova, Italy.
Department of Electrical, Electronic, Telecommunications Eng. and Naval Architecture (DITEN), Via Opera Pia 11a, Genova, 16145, Università degli Studi di Genova, Italy.
Comput Biol Med. 2017 Aug 1;87:335-346. doi: 10.1016/j.compbiomed.2017.06.011. Epub 2017 Jun 12.
In the context of rheumatic diseases, several studies suggest that Magnetic Resonance Imaging (MRI) allows the detection of the three main signs of Rheumatoid Arthritis (RA) at higher sensitivities than available through conventional radiology. The rapid, accurate segmentation of bones is an essential preliminary step for quantitative diagnosis, erosion evaluation, and multi-temporal data fusion. In the present paper, a new, semi-automatic, 3D graph-based segmentation method to extract carpal bone data is proposed. The method is unsupervised, does not employ any a priori model or knowledge, and is adaptive to the individual variability of the acquired data. After selecting one source point inside the Region of Interest (ROI), a segmentation process is initiated, which consists of two automatic stages: a cost-labeling phase and a graph-cutting phase. The algorithm finds optimal paths based on a new cost function by creating a Minimum Path Spanning Tree (MPST). To extract the region, a cut of the obtained tree is necessary. A new criterion of the MPST-cut based on compactness shape factor was conceived and developed. The proposed approach is applied to a large database of 96 T1-weighted MR bone volumes. Performance quality is evaluated by comparing the results with gold-standard bone volumes manually defined by rheumatologists through the computation of metrics extracted from the confusion matrix. Furthermore, comparisons with the existing literature are carried out. The results show that this method is efficient and provides satisfactory performance for bone segmentation on low-field MR volumes.
在风湿性疾病领域，多项研究表明磁共振成像（MRI）比传统放射学更能灵敏地检测出类风湿关节炎（RA）的三个主要征象。快速、准确地分割骨骼是定量诊断、侵蚀评估和多时相数据融合的必要初步步骤。在本文中，提出了一种新的、基于图形的半自动 3D 分割方法，用于提取腕骨数据。该方法是无监督的，不使用任何先验模型或知识，并且适应所获取数据的个体可变性。在选择 ROI 内的一个源点后，启动分割过程，该过程包括两个自动阶段：代价标记阶段和图割阶段。该算法通过创建最小路径生成树（MPST），基于新的代价函数找到最优路径。为了提取区域，需要对获得的树进行切割。基于紧凑度形状因子的 MPST 切割的新准则被构思和开发。该方法应用于一个包含 96 个 T1 加权 MR 骨容积的大型数据库。通过计算混淆矩阵中提取的指标来比较与手动由风湿病学家定义的金标准骨容积的结果，从而评估性能质量。此外，还与现有文献进行了比较。结果表明，该方法在低场 MR 容积的骨骼分割方面效率高，性能令人满意。
