Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the locations of neuronal voxels using image segmentation methods, prior to applying tracing or reconstruction techniques. This preprocessing step is expected to remove noises in the data, thereby leading to improved reconstruction results. In this paper, we proposed to use 3-D convolutional neural networks (CNNs) for segmenting the neuronal microscopy images. Specifically, we designed a novel CNN architecture, that takes volumetric images as the inputs and their voxel-wise segmentation maps as the outputs. The developed architecture allows us to train and predict using large microscopy images in an end-to-end manner. We evaluated the performance of our model on a variety of challenging 3-D microscopy images from different organisms. Results showed that the proposed methods improved the tracing performance significantly when combined with different reconstruction algorithms.

Supplementary data are available at Bioinformatics online.

Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.

A central theme in learning from image data is to develop appropriate representations for the specific task at hand. Thus, a practical challenge is to determine what features are appropriate for specific tasks. For example, in the study of gene expression patterns in Drosophila, texture features were particularly effective for determining the developmental stages from in situ hybridization images. Such image representation is however not suitable for controlled vocabulary term annotation. Here, we developed feature extraction methods to generate hierarchical representations for ISH images. Our approach is based on the deep convolutional neural networks that can act on image pixels directly. To make the extracted features generic, the models were trained using a natural image set with millions of labeled examples. These models were transferred to the ISH image domain. To account for the differences between the source and target domains, we proposed a partial transfer learning scheme in which only part of the source model is transferred. We employed multi-task learning method to fine-tune the pre-trained models with labeled ISH images. Results showed that feature representations computed by deep models based on transfer and multi-task learning significantly outperformed other methods for annotating gene expression patterns at different stage ranges.

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