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

职业：深度稀疏字典上下文模型及其在连接组学图像解析和神经元跟踪中的应用

• 批准号: 1149299
• 负责人: Tolga Tasdizen
• 金额: $ 40.94万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149299&HistoricalAwards=false
• 关键词: CAREER; Deep; sparse; dictionary; context

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.

本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中重建大规模的神经回路。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线路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远未达到人类视觉的准确性和鲁棒性，生物学家仍然局限于研究主要使用人工分析的小神经回路。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，并以有效的方式自动校对这些结果。从电子显微镜体积中重建神经回路需要对这些图像进行逐像素的注释，将其划分为细胞膜、线粒体和突触囊泡等类别，并对单个神经元进行三维分割。这项任务要求极高的精度。即使具有99%的像素精度（对于许多其他应用来说是可以接受的精度），几乎可以肯定的是，由于它们的全局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一个关键瓶颈。在这个项目中，将通过将稀疏字典的表示能力及其易于学习的能力与推理和证明阅读能力相结合，创建一个新的分层模型。人类专家将通过为监督学习和自动生成结果的校对提供基础事实来促进这一过程。深度稀疏字典与使用条件概率连接权的推理相结合可以提供一种非常快速的学习分层模型的方法。为了理解特征表示、推理、对称连接、深层连接和横向连接的相对重要性，我们将研究该模型的几个变体。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。成功与否将在专家注释的真实数据集上进行评估。