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Article

EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization

Pattern Recognition and Machine Learning Group, Computer Engineering School, Costa Rica Institute of Technology, Cartago 30101, Costa Rica
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Academic Editor: Josep Samitier
Biomimetics 2021, 6(2), 37; https://doi.org/10.3390/biomimetics6020037
Received: 16 April 2021 / Revised: 17 May 2021 / Accepted: 28 May 2021 / Published: 1 June 2021
(This article belongs to the Special Issue Bioinspired Intelligence II)
Electron Microscopy Maps are key in the study of bio-molecular structures, ranging from borderline atomic level to the sub-cellular range. These maps describe the envelopes that cover possibly a very large number of proteins that form molecular machines within the cell. Within those envelopes, we are interested to find what regions correspond to specific proteins so that we can understand how they function, and design drugs that can enhance or suppress a process that they are involved in, along with other experimental purposes. A classic approach by which we can begin the exploration of map regions is to apply a segmentation algorithm. This yields a mask where each voxel in 3D space is assigned an identifier that maps it to a segment; an ideal segmentation would map each segment to one protein unit, which is rarely the case. In this work, we present a method that uses bio-inspired optimization, through an Evolutionary-Optimized Segmentation algorithm, to iteratively improve upon baseline segments obtained from a classical approach, called watershed segmentation. The cost function used by the evolutionary optimization is based on an ideal segmentation classifier trained as part of this development, which uses basic structural information available to scientists, such as the number of expected units, volume and topology. We show that a basic initial segmentation with the additional information allows our evolutionary method to find better segmentation results, compared to the baseline generated by the watershed. View Full-Text
Keywords: electron microscopy; segmentation; EM; machine learning; random forests; evolutionary algorithms electron microscopy; segmentation; EM; machine learning; random forests; evolutionary algorithms
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MDPI and ACS Style

Zumbado-Corrales, M.; Esquivel-Rodríguez, J. EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization. Biomimetics 2021, 6, 37. https://doi.org/10.3390/biomimetics6020037

AMA Style

Zumbado-Corrales M, Esquivel-Rodríguez J. EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization. Biomimetics. 2021; 6(2):37. https://doi.org/10.3390/biomimetics6020037

Chicago/Turabian Style

Zumbado-Corrales, Manuel, and Juan Esquivel-Rodríguez. 2021. "EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization" Biomimetics 6, no. 2: 37. https://doi.org/10.3390/biomimetics6020037

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