Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

Abstract

Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.

Accepted by CVPR Workshop on Computer Vision for Microscopy Image Analysis (CVMI)

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• AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training
• Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
• TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation
• RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images
• Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps
• Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation