266 research outputs found

    Watershed merge forest classification for electron microscopy image stack segmentation

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    pre-printAutomated electron microscopy (EM) image analysis techniques can be tremendously helpful for connectomics research. In this paper, we extend our previous work [1] and propose a fully automatic method to utilize inter-section information for intra-section neuron segmentation of EM image stacks. A watershed merge forest is built via the watershed transform with each tree representing the region merging hierarchy of one 2D section in the stack. A section classifier is learned to identify the most likely region correspondence between adjacent sections. The inter-section information from such correspondence is incorporated to update the potentials of tree nodes. We resolve the merge forest using these potentials together with consistency constraints to acquire the final segmentation of the whole stack. We demonstrate that our method leads to notable segmentation accuracy improvement by experimenting with two types of EM image data sets

    Doctor of Philosophy in Computing

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    dissertationImage segmentation is the problem of partitioning an image into disjoint segments that are perceptually or semantically homogeneous. As one of the most fundamental computer vision problems, image segmentation is used as a primary step for high-level vision tasks, such as object recognition and image understanding, and has even wider applications in interdisciplinary areas, such as longitudinal brain image analysis. Hierarchical models have gained popularity as a key component in image segmentation frameworks. By imposing structures, a hierarchical model can efficiently utilize features from larger image regions and make optimal inference for final segmentation feasible. We develop a hierarchical merge tree (HMT) model for image segmentation. Motivated by the application in large-scale segmentation of neuronal structures in electron microscopy (EM) images, our model provides a compact representation of region merging hypotheses and utilizes higher order information for efficient segmentation inference. Taking advantage of supervised learning, our model is free from parameter tuning and outperforms previous state-of-the-art methods on both two-dimensional (2D) and three-dimensional EM image data sets without any change. We also extend HMT to the hierarchical merge forest (HMF) model. By identifying region correspondences, HMF utilizes inter-section information to correct intra-section errors and improves 2D EM segmentation accuracy. HMT is a generic segmentation model. We demonstrate this by applying it to natural image segmentation problems. We propose a constrained conditional model formulation with a globally optimal inference algorithm for HMT and an iterative merge tree sampling algorithm that significantly improves its performance. Experimental results show our approach achieves state-of-the-art accuracy for object-independent image segmentation. Finally, we propose a semi-supervised HMT (SSHMT) model to reduce the high demand for labeled data by supervised learning. We introduce a differentiable unsupervised loss term that enforces consistent boundary predictions and develop a Bayesian learning model that combines supervised and unsupervised information. We show that with a very small amount of labeled data, SSHMT consistently performs close to the supervised HMT with full labeled data sets and significantly outperforms HMT trained with the same labeled subsets

    Watershed merge tree classification for electron microscopy image segmentation

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    pre-printAutomated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy

    Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images

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    Automated sample preparation and electron microscopy enables acquisition of very large image data sets. These technical advances are of special importance to the field of neuroanatomy, as 3D reconstructions of neuronal processes at the nm scale can provide new insight into the fine grained structure of the brain. Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets. In this paper we present a pipeline that provides state-of-the art reconstruction performance while scaling to data sets in the GB-TB range. First, we train a random forest classifier on interactive sparse user annotations. The classifier output is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image. These segmentations are then combined into geometrically consistent 3D objects by segmentation fusion. We provide qualitative and quantitative evaluation of the automatic segmentation and demonstrate large-scale 3D reconstructions of neuronal processes from a 27,000\mathbf{27,000} μm3\mathbf{\mu m^3} volume of brain tissue over a cube of 30  μm\mathbf{30 \; \mu m} in each dimension corresponding to 1000 consecutive image sections. We also introduce Mojo, a proofreading tool including semi-automated correction of merge errors based on sparse user scribbles

    Deep Models for Brain EM Image Segmentation: Novel Insights and improved Performance

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    Motivation: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation. Results: In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015

    Machine Learning Methods for Brain Image Analysis

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    Understanding how the brain functions and quantifying compound interactions between complex synaptic networks inside the brain remain some of the most challenging problems in neuroscience. Lack or abundance of data, shortage of manpower along with heterogeneity of data following from various species all served as an added complexity to the already perplexing problem. The ability to process vast amount of brain data need to be performed automatically, yet with an accuracy close to manual human-level performance. These automated methods essentially need to generalize well to be able to accommodate data from different species. Also, novel approaches and techniques are becoming a necessity to reveal the correlations between different data modalities in the brain at the global level. In this dissertation, I mainly focus on two problems: automatic segmentation of brain electron microscopy (EM) images and stacks, and integrative analysis of the gene expression and synaptic connectivity in the brain. I propose to use deep learning algorithms for the 2D segmentation of EM images. I designed an automated pipeline with novel insights that was able to achieve state-of-the-art performance on the segmentation of the \textit{Drosophila} brain. I also propose a novel technique for 3D segmentation of EM image stacks that can be trained end-to-end with no prior knowledge of the data. This technique was evaluated in an ongoing online challenge for 3D segmentation of neurites where it achieved accuracy close to a second human observer. Later, I employed ensemble learning methods to perform the first systematic integrative analysis of the genome and connectome in the mouse brain at both the regional- and voxel-level. I show that the connectivity signals can be predicted from the gene expression signatures with an extremely high accuracy. Furthermore, I show that only a certain fraction of genes are responsible for this predictive aspect. Rich functional and cellular analysis of these genes are detailed to validate these findings

    Doctor of Philosophy

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    dissertationIn connectomics the goal is to generate a full 3D reconstruction of neurons within the brain. This reconstruction will help neuroscientists better understand how the brain functions and also how it fails in the case of dementia and other neurodegenerative diseases. Attempts for this reconstruction are ongoing at varying levels of resolution from the millimeter scale of magnetic resonance imaging (MRI) to the nanometer scale of electron microscopy. In this dissertation, we develop tools that improve the ability of researchers to trace neurons through a volume in vitro at a resolution sufficient for the identification of synapses. The first toolset will speed up the process for generating training datasets in newly acquired volumes to be used in supervised learning methods. Current methods of training dataset generation require dense labeling by a trained research scientist over hundreds of hours. This toolset will reduce the time required for training data generation by using sparse sampling and reduce the cost of that time by using a priori knowledge of the structure to allow for less specialized researchers to assist in the process. The second toolset is targeted at speeding up the correction of errors introduced in automatic neuron segmentation methods. Often referred to as proofreading, current methods require significant user input and fail to fully incorporate the information generated by the automatic segmentation method. I will use novel 2D and 3D visualization techniques to take advantage of the information generated during automatic segmentation into the proofreading process. This toolset will consist of two different applications, with one process targeting 2D proofreading with 3D linking and the other process targeting direct 3D proofreading
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