[Paper Review] Fluorescence Microscopy Image Segmentation Using Convolutional Neural Network With Generative Adversarial Networks.

Chichen Fu, Soonam Lee+5 more|arXiv (Cornell University)|Jan 22, 2018

AI in cancer detection23 references3 citations

TL;DR

This paper proposes a 3D nuclei segmentation method using a 3D convolutional neural network (CNN) trained on synthetic data generated by a generative adversarial network (GAN), overcoming the challenge of limited annotated fluorescence microscopy volumes. The approach achieves robust segmentation across diverse datasets without requiring extensive manual annotation.

ABSTRACT

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

Motivation & Objective

• To address the scarcity of annotated 3D fluorescence microscopy volumes for training deep learning models.
• To develop a method that reduces reliance on time-consuming manual annotation of 3D nuclei segmentation ground truth.
• To generate realistic synthetic 3D microscopy volumes and corresponding ground truth using a GAN for training deep learning models.
• To evaluate the performance of a 3D CNN trained on synthetic data for nuclei segmentation across diverse biological datasets.

Proposed method

• A 3D convolutional neural network (CNN) is trained to perform nuclei segmentation on 3D fluorescence microscopy images.
• A generative adversarial network (GAN) is used to automatically generate synthetic 3D microscopy volumes and their corresponding ground truth segmentation masks.
• The GAN is trained to produce realistic 3D image volumes that mimic the appearance and structural complexity of real fluorescence microscopy data.
• Synthetic data are used to pre-train or fine-tune the 3D CNN, reducing dependence on real annotated data.
• The training process leverages adversarial loss to improve the realism of generated images and segmentation consistency.
• The method is evaluated on multiple real 3D microscopy datasets to assess generalization and segmentation accuracy.

Experimental results

Research questions

• RQ1 Can a GAN-generated synthetic dataset effectively train a 3D CNN for nuclei segmentation in fluorescence microscopy?
• RQ2 How does the performance of a 3D CNN trained on synthetic data compare to models trained on real annotated data?
• RQ3 To what extent can the synthetic data generalize across different biological samples and imaging conditions?
• RQ4 Does the use of GAN-generated data reduce the need for manual annotation while maintaining segmentation accuracy?

Key findings

• The proposed method successfully segments nuclei in 3D fluorescence microscopy images across multiple datasets without requiring manual annotation of training data.
• The use of GAN-generated synthetic data enables effective training of a 3D CNN, achieving high segmentation accuracy.
• The model generalizes well to diverse biological samples, demonstrating robustness to variations in imaging conditions.
• The approach significantly reduces the dependency on large-scale manual annotation, which is a major bottleneck in 3D microscopy analysis.