Learning-based Segmentation for Connectomics

Recent advances in electron microscopy techniques make it possible to acquire highresolution, isotropic volume images of neural circuitry. In connectomics, neuroscientists seek to obtain the circuit diagram involving all neurons and synapses in such a volume image. Mapping neuron connectivity requires tracing each and every neural process through terabytes of image data. Due to the size and complexity of these volume images, fully automated analysis methods are desperately needed. In this thesis, I consider automated, machine learning-based neurite segmentation approaches based on a simultaneous merge decision of adjacent supervoxels. - Given a learned likelihood of merging adjacent supervoxels, Chapter 4 adapts a probabilistic graphical model which ensures that merge decisions are consistent and the surfaces of final segments are closed. This model can be posed as a multicut optimization problem and is solved with the cutting-plane method. In order to scale to large datasets, a fast search for (and good choice of) violated cycle constraints is crucial. Quantitative experiments show that the proposed closed-surface regularization significantly improves segmentation performance. - In Chapter 5, I investigate whether the edge weights of the previous model can be chosen to minimize the loss with respect to non-local segmentation quality measures (e.g. Rand Index). Suitable w are obtained from a structured learning approach. In the Structured Support Vector Machine formulation, a novel fast enumeration scheme is used to find the most violated constraint. Quantitative experiments show that structured learning can improve upon unstructured methods. Furthermore, I introduce a new approximate, hierarchical and blockwise optimization approach for large-scale multicut segmentation. Using this method, high-quality approximate solutions for large problem instances are found quickly. - Chapter 6 introduces another novel approximate scheme for multicut segmentation -- Cut, Glue&Cut -- which is based on the move-making paradigm. First, the graph is recursively partitioned into small regions (cut phase). Then, for any two adjacent regions, alternative cuts of these two regions define possible moves (glue&cut phase). The proposed algorithm finds segmentations that are { as measured by a loss function { as close to the ground-truth as the global optimum found by exact solvers, while being significantly faster than existing methods. - In order to jointly label resulting segments as well as to label the boundaries between segments, Chapter 7 proposes the Asymmetric Multi-way Cut model, a variant of Multi-way Cut. In this new model, within-class cuts are allowed for some labels, while being forbidden for other labels. Qualitative experiments show when such a formulation can be beneficial. In particular, an application to joint neurite and cell organelle labeling in EM volume images is discussed. - Custom software tools that can cope with the large data volumes common in the field of connectomics are a prerequisite for the implementation and evaluation of novel segmentation techniques. Chapter 3 presents version 1.0 of ilastik, a joint effort of multiple researchers. I have co-written its volume viewing component, volumina. ilastik provides an interactive pixel classification work ow on largerthan-RAM datasets as well as a semi-automated segmentation module useful for acquiring gold standard segmentations. Furthermore, I describe new software for dealing with hierarchies of cell complexes as well as for blockwise image processing operations on large datasets. The different segmentation methods presented in this thesis provide a promising direction towards reaching the required reliability as well as the required data throughput necessary for connectomics applications

Machine learning of hierarchical clustering to segment 2D and 3D images

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.Comment: 15 pages, 8 figure

Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model

This thesis presents a new mathematical model for segmenting volume images. The model is an energy function defined on the state space of all possibilities to remove or preserve splitting faces from an initial over-segmentation of the 3D image into supervoxels. It decomposes into potential functions that are learned automatically from a small amount of empirical training data. The learning is based on features of the distribution of gray values in the volume image and on features of the geometry and topology of the supervoxel segmentation. To be able to extract these features from large 3D images that consist of several billion voxels, a new algorithm is presented that constructs a suitable representation of the geometry and topology of volume segmentations in a block-wise fashion, in log-linear runtime (in the number of voxels) and in parallel, using only a prescribed amount of memory. At the core of this thesis is the optimization problem of finding, for a learned energy function, a segmentation with minimal energy. This optimization problem is difficult because the energy function consists of 3rd and 4th order potential functions that are not submodular. For sufficiently small problems with 10,000 degrees of freedom, it can be solved to global optimality using Mixed Integer Linear Programming. For larger models with 10,000,000 degrees of freedom, an approximate optimizer is proposed and compared to state-of-the-art alternatives. Using these new techniques and a unified data structure for multi-variate data and functions, a complete processing chain for segmenting large volume images, from the restoration of the raw volume image to the visualization of the final segmentation, has been implemented in C++. Results are shown for an application in neuroscience, namely the segmentation of a part of the inner plexiform layer of rabbit retina in a volume image of 2048 x 1792 x 2048 voxels that was acquired by means of Serial Block Face Scanning Electron Microscopy (Denk and Horstmann, 2004) with a resolution of 22nm x 22nm x 30nm. The quality of the automated segmentation as well as the improvement over a simpler model that does not take geometric context into account, are confirmed by a quantitative comparison with the gold standard

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

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 $\mathbf{27,000}$ $\mathbf{\mu m^3}$ volume of brain tissue over a cube of $\mathbf{30 \; \mu m}$ 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

Annotating Synapses in Large EM Datasets

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders

Doctor of Philosophy in Computing

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

Brain MR Image Segmentation: From Multi-Atlas Method To Deep Learning Models

Quantitative analysis of the brain structures on magnetic resonance (MR) images plays a crucial role in examining brain development and abnormality, as well as in aiding the treatment planning. Although manual delineation is commonly considered as the gold standard, it suffers from the shortcomings in terms of low efficiency and inter-rater variability. Therefore, developing automatic anatomical segmentation of human brain is of importance in providing a tool for quantitative analysis (e.g., volume measurement, shape analysis, cortical surface mapping). Despite a large number of existing techniques, the automatic segmentation of brain MR images remains a challenging task due to the complexity of the brain anatomical structures and the great inter- and intra-individual variability among these anatomical structures. To address the existing challenges, four methods are proposed in this thesis. The first work proposes a novel label fusion scheme for the multi-atlas segmentation. A two-stage majority voting scheme is developed to address the over-segmentation problem in the hippocampus segmentation of brain MR images. The second work of the thesis develops a supervoxel graphical model for the whole brain segmentation, in order to relieve the dependencies on complicated pairwise registration for the multi-atlas segmentation methods. Based on the assumption that pixels within a supervoxel are supposed to have the same label, the proposed method converts the voxel labeling problem to a supervoxel labeling problem which is solved by a maximum-a-posteriori (MAP) inference in Markov random field (MRF) defined on supervoxels. The third work incorporates attention mechanism into convolutional neural networks (CNN), aiming at learning the spatial dependencies between the shallow layers and the deep layers in CNN and producing an aggregation of the attended local feature and high-level features to obtain more precise segmentation results. The fourth method takes advantage of the success of CNN in computer vision, combines the strength of the graphical model with CNN, and integrates them into an end-to-end training network. The proposed methods are evaluated on public MR image datasets, such as MICCAI2012, LPBA40, and IBSR. Extensive experiments demonstrate the effectiveness and superior performance of the three proposed methods compared with the other state-of-the-art methods

Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images

Identifying complex neural circuitry from electron microscopic (EM) images may help unlock the mysteries of the brain. However, identifying this circuitry requires time-consuming, manual tracing (proofreading) due to the size and intricacy of these image datasets, thus limiting state-of-the-art analysis to very small brain regions. Potential avenues to improve scalability include automatic image segmentation and crowd sourcing, but current efforts have had limited success. In this paper, we propose a new strategy, focused proofreading, that works with automatic segmentation and aims to limit proofreading to the regions of a dataset that are most impactful to the resulting circuit. We then introduce a novel workflow, which exploits biological information such as synapses, and apply it to a large dataset in the fly optic lobe. With our techniques, we achieve significant tracing speedups of 3-5x without sacrificing the quality of the resulting circuit. Furthermore, our methodology makes the task of proofreading much more accessible and hence potentially enhances the effectiveness of crowd sourcing