Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets. We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators. To our knowledge, this work represents the first published approach to automating the creation of cell models with this level of structural detail.
Modern biological electron microscopy produces nanoscale images from biological samples of unprecedented volume, and researchers now face the problem of making use of the data. Image segmentation has played a fundamental role in EM image analysis for decades, but challenges from biological EM have spurred interest and rapid advances in computer vision for automating the segmentation process. In this paper, we demonstrate dense cellular segmentation as a method for generating rich 3D models of tissues and their constituent cells and organelles from scanning electron microscopy images. We describe how to use ensembles of 2D-3D neural networks to compute dense cellular segmentations of cells and organelles inside two human platelet tissue samples. We conclude by discussing ongoing challenges for realizing practical dense cellular segmentation algorithms. The data and code used in this paper, as well as example notebooks, are available at leapmanlab. github.io/dense-cell.Biomedical researchers use electron microscopy (EM) to image cells, organelles, and their constituents at the nanoscale. The serial block-face scanning electron microscope (SBF-SEM) 1 uses automated serial sectioning techniques to rapidly produce gigavoxel image volumes and beyond. This rapid growth in throughput challenges traditional image analytic workflows for EM, which rely on trained humans to identify salient image features. Highthroughput EM offers to revolutionize systems biology by providing nanoscale structural detail across macroscopic tissue regions, but analyses of such datasets in their entirety will be infeasibly expensive and time-consuming until analytic bottlenecks are automated. A fundamental component of common EM image anaylsis workflows is segmentation, which groups image voxels together into labeled regions that correspond to image content. For semantic segmentation, each voxel is assigned an object classification label, such as cell or mitochondrion. In this paper we introduce a dense cellular segmentation task, illustrated in Figure 1, which seeks to segment the entirety of an image volume with multiple, detailed class labels. Dense semantic segmentation is vital for systems biologists seeking to create semantically-rich 3D models of cells and subcellular structure interconnected within tissue environments. Manually performing dense segmentation tasks for EM volumes is tedious and infeasible at scale for new highthroughput microscopes. However, automating dense segmentation for EM is challenging due to the image complexity of biological structures at the nanoscale. An image with little noise and high contrast between features may be accurately segmented with simple methods such as thresholding 2 , while accurate segmentation of complicated images with multiscale features, noise, and textural content remains an open problem for many tasks of interest to biomedicine. Current state-of-the-art computational solutions use convolutional neural network architectures to solve problems on a per-task basis. The field of connectomics h...
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