Semantic Image Segmentation of Cell Volumes Using 3D U-Net Convolutional Neural Networks

Abstract

Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. Traditionally image segmentation was used on 2D imaging data, but due to the increased usage of 3D volumetric data there is a need for 3D segmentation techniques that could utilize spatial information contained in these volumes. One of the fields where there is a great amount of 3D data is microscopy. This paper introduces convolutional neural network based on 3D U-net architecture for segmentation of confocal microscopy images of cells in an in vitro sprouting angiogenesis model. Developed model contains 4 layers where each encoder block contains two 3D convolutional layers, Batch Normalization, ReLU activation function and 3D max pooling layer, while each decoder block contains upconvolution, skip connections and two 3D convolutional layers. Preprocessing of this data resulted in the volumes of shape 256×256×256 voxels which were used for training of the developed model. The model achieves great segmentation results as showed by Jaccard index value of 94.52% and Dice coefficient value of 99.31% compared to the preprocessed dataset. Even when segmentation results are compared to the original dataset, model still achieves respectable results of 84.22% Jaccard index and 88.18% Dice coefficient. This introduction of automatic 3D image segmentation could greatly reduce the time required for data preparation, while achieving high degree of segmentation accuracy.