An Improved Watershed Segmentation Method for Electron Microscopy Images

Abstract

The analysis of nerve tissues is very helpful to the study of the nervous system. With the development and application of high-throughput technology, it becomes more important to realize the automated batch segmentation of nerve tissues in the electron microscopy(EM) image. Aiming at this, we propose an improved watershed segmentation method based on multiple deep-CNN models. Firstly, we use the classical watershed segmentation algorithm to obtain some candidate edge pixels in an original EM image. Secondly, the local regions of these candidate pixels are respectively extracted as the samples to be predicted. Thirdly, all candidate edge pixels are judged by a trained pixel classifier, of which the architecture is a deep convolutional neural network. Then, an initial segmentation result is generated based on the judgements. Finally, a complete segmentation image can be obtained after the morphological processing. The training and testing of model are finished on the ISBI dataset. The application of model is realized by merging the prediction results of three models with different scales. Experiments show that our method can spend less cost to have an approximate segmentation result, compared with other methods. Besides, our method can also show a better performance in the details.