| Autor: |
Joyce J; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Chalavadi R; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory.; Johns Hopkins University Krieger School of Arts and Sciences., Chan J; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory.; Johns Hopkins University Krieger School of Arts and Sciences., Tanna S; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory.; Johns Hopkins University Krieger School of Arts and Sciences., Xenes D; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Kuo N; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Rose V; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Matelsky J; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Kitchell L; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Bishop C; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Rivlin PK; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Villafañe-Delgado M; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory., Wester B; Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory. |
| Abstrakt: |
The immense scale and complexity of neuronal electron microscopy (EM) datasets pose significant challenges in data processing, validation, and interpretation, necessitating the development of efficient, automated, and scalable error-detection methodologies. This paper proposes a novel approach that employs mesh processing techniques to identify potential error locations near neuronal tips. Error detection at tips is a particularly important challenge since these errors usually indicate that many synapses are falsely split from their parent neuron, injuring the integrity of the connectomic reconstruction. Additionally, we draw implications and results from an implementation of this error detection in a semi-automated proofreading pipeline. Manual proofreading is a laborious, costly, and currently necessary method for identifying the errors in the machine learning based segmentation of neural tissue. This approach streamlines the process of proofreading by systematically highlighting areas likely to contain inaccuracies and guiding proofreaders towards potential continuations, accelerating the rate at which errors are corrected. |
