Biomedical and Multimedia Information Technology Group, School of Information Technologies, The University of Sydney, Sydney, Australia.
Biomedical and Multimedia Information Technology Group, School of Information Technologies, The University of Sydney, Sydney, Australia.
Comput Methods Programs Biomed. 2018 Jun;159:211-222. doi: 10.1016/j.cmpb.2018.03.018. Epub 2018 Mar 21.
It can be challenging to delineate the target object in anatomical imaging when the object boundaries are difficult to discern due to the low contrast or overlapping intensity distributions from adjacent tissues.
We propose a topo-graph model to address this issue. The first step is to extract a topographic representation that reflects multiple levels of topographic information in an input image. We then define two types of node connections - nesting branches (NBs) and geodesic edges (GEs). NBs connect nodes corresponding to initial topographic regions and GEs link the nodes at a detailed level. The weights for NBs are defined to measure the similarity of regional appearance, and weights for GEs are defined with geodesic and local constraints. NBs contribute to the separation of topographic regions and the GEs assist the delineation of uncertain boundaries. Final segmentation is achieved by calculating the relevance of the unlabeled nodes to the labels by the optimization of a graph-based energy function. We test our model on 47 low contrast CT studies of patients with non-small cell lung cancer (NSCLC), 10 contrast-enhanced CT liver cases and 50 breast and abdominal ultrasound images. The validation criteria are the Dice's similarity coefficient and the Hausdorff distance.
Student's t-test show that our model outperformed the graph models with pixel-only, pixel and regional, neighboring and radial connections (p-values <0.05).
Our findings show that the topographic representation and topo-graph model provides improved delineation and separation of objects from adjacent tissues compared to the tested models.
当目标对象的边界因对比度低或与相邻组织的强度分布重叠而难以分辨时，在解剖成像中对其进行描绘可能具有挑战性。
我们提出了一种拓扑模型来解决这个问题。第一步是提取反映输入图像中多个层次拓扑信息的拓扑表示。然后，我们定义了两种类型的节点连接——嵌套分支（NB）和测地线边缘（GE）。NB 连接对应初始拓扑区域的节点，GE 连接详细层次的节点。NB 的权重用于衡量区域外观的相似性，GE 的权重则根据测地线和局部约束来定义。NB 有助于分离拓扑区域，GE 有助于描绘不确定的边界。最终的分割是通过计算无标签节点与标签之间的相关性来实现的，这是通过优化基于图的能量函数来实现的。我们在 47 例非小细胞肺癌（NSCLC）患者的低对比度 CT 研究、10 例增强 CT 肝脏病例和 50 例乳腺和腹部超声图像上测试了我们的模型。验证标准是 Dice 相似系数和 Hausdorff 距离。
学生 t 检验表明，与仅像素、像素和区域、相邻和放射状连接的图模型相比，我们的模型表现更好（p 值<0.05）。
我们的研究结果表明，与测试的模型相比，拓扑表示和拓扑模型提供了对物体与相邻组织的更好的描绘和分离。
