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

该提案的研究目标是创建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回路。电子显微镜是在单个神经元和突触水平上重建神经回路的关键技术，也称为连接组学。虽然连接组学的一个重要动机是为神经回路模型提供解剖学基础，但在单个细胞水平上破译神经布线图的能力在许多神经退行性疾病的研究中也很重要。最先进的图像分析解决方案仍然远远没有达到人类视觉的准确性和鲁棒性，生物学家仍然局限于主要使用手动分析来研究小型神经回路。所提出的计算模型将为生物学家提供一种工具，用于分割单个神经元并在非常大的电子显微镜体积中检测其他结构，如突触，并以时间有效的方式证明阅读这些自动产生的结果。从电子显微镜体积重建神经回路涉及将这些图像逐像素注释为细胞膜，线粒体和突触囊泡以及单个神经元的三维分割。这项工作需要极高的精度。即使具有99%的像素精度（对于许多其他应用来说是可接受的精度），几乎可以肯定的是，体积中的几乎每个神经元都将由于其全局、树状结构和相应的大表面积而被错误地分割。因此，缺乏可靠的自动化解决方案是连接组学领域的关键瓶颈。在这个项目中，将创建一个新的层次模型，结合稀疏字典的表示能力和他们的易于学习的推理和证明阅读能力。人类专家将通过为监督学习提供基础事实和自动生成结果的证明阅读来为该过程做出贡献。深度稀疏字典与使用条件概率的连接权重进行推理的组合可以提供一种非常快速的学习分层模型的方法。将研究模型的几个变体，以了解特征表示、推理、对称连接、深层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对象分类和图像解析问题以及连接组学数据集。成功与否将根据专家注释的真实的数据集进行评估。

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.