Deep learning-based identification of sub-nuclear structures in FIB-SEM images

TL;DR

This paper introduces a deep learning model that accurately identifies sub-nuclear structures in 3D FIB-SEM images, enabling detailed cellular analysis with high precision.

Contribution

A novel supervised convolutional neural network approach for sub-nuclear structure identification in volumetric cellular imaging data.

Findings

Achieved 90% accuracy in identifying sub-nuclear structures

Successfully segmented entire chromosomes in C. elegans gonads

Provided detailed model architecture and optimization strategies

Abstract

Three-dimensional volumetric imaging of cells allows for in situ visualization, thus preserving contextual insights into cellular processes. Despite recent advances in machine learning methods, morphological analysis of sub-nuclear structures have proven challenging due to both the shallow contrast profile and the technical limitation in feature detection. Here, we present a convolutional neural network, supervised deep learning-based approach which can identify sub-nuclear structures with 90% accuracy. We develop and apply this model to C. elegans gonads imaged using focused ion beam milling combined with scanning electron microscopy resulting in the accurate identification and segmentation of all sub-nuclear structures including entire chromosomes. We discuss in depth the architecture, parameterization, and optimization of the deep learning model, as well as provide evaluation metrics to assess the quality of the network prediction. Lastly, we highlight specific aspects of the model that can be optimized for its broad application to other volumetric imaging data as well as in situ cryo-electron tomography.