Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images

Tracking of plant cells in images obtained by microscope is a challenging problem due to biological phenomena such as large number of cells, non-uniform growth of different layers of the tightly packed plant cells and cell division. Moreover, images in deeper layers of the tissue being noisy and unavoidable systemic errors inherent in the imaging process further complicates the problem. In this paper, we propose a novel learning-based method that exploits the tightly packed three-dimensional cell structure of plant cells to create a three-dimensional graph in order to perform accurate cell tracking. We further propose novel algorithms for cell division detection and effective three-dimensional registration, which improve upon the state-of-the-art algorithms. We demonstrate the efficacy of our algorithm in terms of tracking accuracy and inference-time on a benchmark dataset.

Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

Segmentation of 3D images is a fundamental problem in biomedical image analysis. Deep learning (DL) approaches have achieved state-of-the-art segmentation perfor- mance. To exploit the 3D contexts using neural networks, known DL segmentation methods, including 3D convolution, 2D convolution on planes orthogonal to 2D image slices, and LSTM in multiple directions, all suffer incompatibility with the highly anisotropic dimensions in common 3D biomedical images. In this paper, we propose a new DL framework for 3D image segmentation, based on a com- bination of a fully convolutional network (FCN) and a recurrent neural network (RNN), which are responsible for exploiting the intra-slice and inter-slice contexts, respectively. To our best knowledge, this is the first DL framework for 3D image segmentation that explicitly leverages 3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal Structure Segmentation Challenge and in-house image stacks for 3D fungus segmentation, our approach achieves promising results comparing to the known DL-based 3D segmentation approaches.