CAREER: Deep sparse dictionary context models and their application to image parsing and neuron tracking for connectomics

这项提议的研究目标是创造新的计算算法和图像处理工具，使生物学家能够从电子显微镜体积重建大规模神经电路。电子显微镜是在单个神经元和突触水平上重建神经电路的关键技术，也被称为连接学。虽然连接学的一个重要动机是为神经电路模型提供解剖学基础，但在单个细胞水平上破译神经连接图的能力在许多神经退行性疾病的研究中也是重要的。最先进的图像分析解决方案仍然远远不能达到人类视觉的准确性和健壮性，生物学家仍然局限于使用主要人工分析来研究微小的神经回路。提出的计算模型将为生物学家提供一种工具，用于在非常大的电子显微镜体积中分割单个神经元和检测其他结构，如突触，并以高效的方式自动校对这些结构。从电子显微镜体积重建神经电路涉及到将这些图像逐个像素地注释为细胞膜、线粒体和突触小泡等类别，并在三维中分割单个神经元。这项任务要求极高的准确度。即使有99%的像素精度，对于许多其他应用来说，这是一个可以接受的精度，但几乎可以肯定的是，由于其全局的树状结构和相应的大表面积，体积中的几乎每个神经元都将被错误分割。因此，缺乏可靠的自动化解决方案是连接学领域的一个关键瓶颈。在这个项目中，将通过结合稀疏词典的表示能力和易学性以及推理和校对能力来创建一个新的层次模型。人类专家将通过为监督学习和对自动产生的结果进行校对提供基本事实，为这一进程做出贡献。深度稀疏词典与使用条件概率中的连接权重进行推理的组合可以提供一种非常快速的方法来学习分层模型。将研究该模型的几个变体，以了解特征表示、推理、对称连接、深度连接和横向连接的相对重要性。该模型将应用于计算机视觉中的一般目标分类和图像分析问题，以及连接学数据集。成功将在经过专家注释的真实数据集上进行评估。

The research objective of this proposal is to create novel computational algorithms and image processing tools that will make it possible for biologists to reconstruct large-scale neural circuits from electron microscopy volumes. Electron microscopy is a key technology in reconstruction of neural circuits at the level of individual neurons and synapses, also known as connectomics. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. State-of-the-art image analysis solutions are still far from the accuracy and robustness of human vision and biologists are still limited to studying small neural circuits using mostly manual analysis. The proposed computational models will provide biologists a tool for segmenting individual neurons and detecting other structures such as synapses in very large electron microscopy volumes, and proof reading these automatically produced results in a time efficient manner.Reconstruction of a neural circuit from an electron microscopy volume involves pixel-by-pixel annotation of these images into classes such as cell membrane, mitochondria and synaptic vesicles and the segmentation of individual neurons in three dimensions. This task demands extremely high accuracy. Even with 99% pixel accuracy, an acceptable accuracy for many other applications, it is virtually certain that almost every neuron in a volume will be incorrectly segmented due to their global, tree-like structure and correspondingly large surface area. Therefore, lack of reliable automated solutions is a critical bottleneck in the field of connectomics. In this project, a novel hierarchical model will be created by combining the representation power of sparse dictionaries and their ease of learning with an inference and proof reading capability. Human experts will contribute to the process by providing ground truth for supervised learning and proof reading of automatically produced results. The combination of deep sparse dictionaries with inference using connection weights from conditional probabilities can provide a very fast way to learn hierarchical models. Several variants of the model will be studied for understanding the relative importance of feature representation, inference, symmetric connections, deep and lateral connections. The model will be applied to general object classification and image parsing problems in computer vision as well as connectomics datasets. Success will be evaluated on real datasets annotated by experts.