论文标题
用卷积神经网络生长的区域用于生物医学图像分割
Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation
论文作者
论文摘要
在本文中，我们提出了一种方法，该方法使用卷积神经网络（CNN）通过迭代增长的每个坐标方向进行预测的掩模区域进行分割。 CNN用于预测中心像素图像瓷砖中中心像素的小邻域中的类概率得分。我们在CNN概率分数上使用阈值来确定是否将像素添加到该区域，并继续迭代，直到没有将新像素添加到该区域为止。我们的方法能够达到高分子的准确性并保留生物学上现实的形态特征，同时利用少量训练数据并维持计算效率。使用驱动器数据库中的视网膜血管图像，我们发现我们的方法比完全卷积的语义分割CNN更准确，用于几种评估指标。
In this paper we present a methodology that uses convolutional neural networks (CNNs) for segmentation by iteratively growing predicted mask regions in each coordinate direction. The CNN is used to predict class probability scores in a small neighborhood of the center pixel in a tile of an image. We use a threshold on the CNN probability scores to determine whether pixels are added to the region and the iteration continues until no new pixels are added to the region. Our method is able to achieve high segmentation accuracy and preserve biologically realistic morphological features while leveraging small amounts of training data and maintaining computational efficiency. Using retinal blood vessel images from the DRIVE database we found that our method is more accurate than a fully convolutional semantic segmentation CNN for several evaluation metrics.
