Sergiy Popovych will present his FPO "Self-Supervised Metric Learning for Alignment of Petascale Connectomics Datasets" on Tuesday, June 21, 2022 at 6:00 PM via Zoom.
Location: Zoom link: https://princeton.zoom.us/j/95852144614
The members of Sergiy’s committee are as follows:
Examiners: H. Sebastian Seung (Adviser), Karthik Narasimhan, Stephan Saalfeld (Janelia Research Campus)
Readers: Kai Li, Viren Jain (Google)
A copy of his thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.
Everyone is invited to attend his talk.
Abstract follows below:
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.
Development of the presented pipeline constitutes a team effort by Seunglab members. Thomas Macrina and Barak Nehoran took lead roles in development of global alignment and vector voting based error recovery. The author of this dissertation Sergiy Popovych has taken a lead role in image pair alignment, seethrough‐based error recovery, as well as the development and architecture of the pipeline software stack. Nico Kemnitz, Manuel Castro, and Dodam Ih have contributed a large variety of software and research components.