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Title: Self-Supervised Metric Learning for Alignment of Petascale Connectomics Datasets
Authors: Popovych, Sergiy
Advisors: Seung, Sebastian H
Contributors: Computer Science Department
Subjects: Artificial intelligence
Issue Date: 2022
Publisher: Princeton, NJ : Princeton University
Abstract: The reconstruction of neural circuits from serial section electron microscopy(ssEM) images is being accelerated by automatic image segmentation methods. These methods are often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. The problem of 3D stack alignment can be divided into two subproblems – image pair alignment and alignment globalization. Image pair alignment is . This dissertation presents a computational pipeline for aligning ssEM images with several key elements. First, a self-supervised convolutional net are trained via metric learning to encode and align image pairs, followed by iterative finetuning of alignment. Second, a procedure called vector voting is used to remove outliers, further increasing robustness to image defects. Third, for speedup the series is divided into blocks that are distributed to computational workers for alignment. Fourth, the blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. The pipeline is used to align a female adult fly brain dataset, as well as a cubic millimeter of mouse visual cortex and is publicly available through two open source Python packages.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Computer Science

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