Learned versus Hand-Designed Feature Representations for 3d Agglomeration

For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh segmentation, shape retrieval, or neuron fragment agglomeration, there remains a strong reliance on hand-designed feature descriptors. In this paper, we evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes

Guided Proofreading of Automatic Segmentations for Connectomics

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5x faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at http://rhoana.org/guidedproofreading/supplemental.pd

Learning Instance Segmentation from Sparse Supervision

Instance segmentation is an important task in many domains of automatic image processing, such as self-driving cars, robotics and microscopy data analysis. Recently, deep learning-based algorithms have brought image segmentation close to human performance. However, most existing models rely on dense groundtruth labels for training, which are expensive, time consuming and often require experienced annotators to perform the labeling. Besides the annotation burden, training complex high-capacity neural networks depends upon non-trivial expertise in the choice and tuning of hyperparameters, making the adoption of these models challenging for researchers in other fields. The aim of this work is twofold. The first is to make the deep learning segmentation methods accessible to non-specialist. The second is to address the dense annotation problem by developing instance segmentation methods trainable with limited groundtruth data. In the first part of this thesis, I bring state-of-the-art instance segmentation methods closer to non-experts by developing PlantSeg: a pipeline for volumetric segmentation of light microscopy images of biological tissues into cells. PlantSeg comes with a large repository of pre-trained models and delivers highly accurate results on a variety of samples and image modalities. We exemplify its usefulness to answer biological questions in several collaborative research projects. In the second part, I tackle the dense annotation bottleneck by introducing SPOCO, an instance segmentation method, which can be trained from just a few annotated objects. It demonstrates strong segmentation performance on challenging natural and biological benchmark datasets at a very reduced manual annotation cost and delivers state-of-the-art results on the CVPPP benchmark. In summary, my contributions enable training of instance segmentation models with limited amounts of labeled data and make these methods more accessible for non-experts, speeding up the process of quantitative data analysis

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly’s brain