Zhu Fei, Liu Quan, Fu Yuchen, Shen Bairong
Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China; School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China.
School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China.
PLoS One. 2014 Mar 13;9(3):e90873. doi: 10.1371/journal.pone.0090873. eCollection 2014.
The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases.
电子显微镜（EM）图像中结构的分割对于神经生物学研究非常重要。低分辨率的神经元EM图像包含噪声，并且通常可供分割的特征很少，因此应用传统方法从EM图像中识别神经元结构并不成功。因此，我们在本研究中提出了一种多尺度融合结构边界检测算法。在该算法中，我们首先生成EM图像高斯金字塔，然后在金字塔的每个级别上，利用高斯函数的拉普拉斯算子（LoG）来获取结构边界，最后通过融合算法组装检测到的边界以获得组合的神经元结构图像。由于获得的神经元结构通常存在间隙，我们提出了一种基于强化学习的边界修正方法来连接检测到的边界中的间隙。我们使用从强化学习中衍生出的基于SARSA（λ）的曲线行进和修正方法来修复不完整的曲线。使用该算法，一个移动点从不完整曲线的一端开始，在图像中行进，决策由近似曲线模型监督，目的是最小化连接成本直到间隙闭合。我们的方法提供了稳定且高效的结构分割。使用来自ISBI 2012的30幅EM图像的测试结果表明，我们的两种方法，即有或没有边界修正的方法，都比六种传统边界检测方法表现更好。特别是，修正后，作为结构分割期间最重要性能指标的兰德误差和翘曲误差降低到了非常低的值。与ISBI 2012的基准方法和最近开发的方法的比较也表明，我们的方法在准确识别EM图像中的子结构方面表现更好，因此对于识别与脑部疾病相关的成像特征很有用。
