论文标题
基于细分的方法与大脑中线描述的动态编程结合
Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
论文作者
论文摘要
中线相关的病理图像特征对于评估脑部或创伤性脑损伤引起的脑压缩的严重程度至关重要。自动化的中线描述不仅改善了患有中风症状或头部外伤患者的评估和临床决策，而且还减少了诊断时间。然而，大多数以前的方法通过定位解剖点来对中线进行建模，这些点很难在严重的情况下检测到甚至缺失。在本文中，我们将大脑中线描述作为分段任务，并提出了三阶段的框架。提出的框架首先将输入CT图像对齐到标准空间中。然后，对齐的图像通过与坐标层和级联Artrouscconv模块集成的中线检测网络（MD-NET）处理，以获得概率图。最后，我们将最佳的中线选择作为探路问题，以解决中线描述不连续性的问题。实验结果表明，我们提出的框架可以在一个内部数据集和一个公共数据集上实现卓越的性能。
The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI). The automated midline delineation not only improves the assessment and clinical decision making for patients with stroke symptoms or head trauma but also reduces the time of diagnosis. Nevertheless, most of the previous methods model the midline by localizing the anatomical points, which are hard to detect or even missing in severe cases. In this paper, we formulate the brain midline delineation as a segmentation task and propose a three-stage framework. The proposed framework firstly aligns an input CT image into the standard space. Then, the aligned image is processed by a midline detection network (MD-Net) integrated with the CoordConv Layer and Cascade AtrousCconv Module to obtain the probability map. Finally, we formulate the optimal midline selection as a pathfinding problem to solve the problem of the discontinuity of midline delineation. Experimental results show that our proposed framework can achieve superior performance on one in-house dataset and one public dataset.
