WikiSysop: Created page with "== Citation == de Isidro-Gómez, Federico P. / Vilas, J. L. / Losana, P. / Carazo, J. M. / Sorzano, Carlos Oscar Sanchez. A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108056 == Abstract == Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general..."

2024-07-30T06:15:56Z

Created page with "== Citation == de Isidro-Gómez, Federico P. / Vilas, J. L. / Losana, P. / Carazo, J. M. / Sorzano, Carlos Oscar Sanchez. A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108056 == Abstract == Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general..."

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== Citation ==

de Isidro-Gómez, Federico P. / Vilas, J. L. / Losana, P. / Carazo, J. M. / Sorzano, Carlos Oscar Sanchez. A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108056

== Abstract ==

Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general
context, including cellular in situ observations. The approach starts by collecting a set of images at different projection directions by tilting the specimen stage inside
the microscope. Therefore, a crucial preliminary step is to precisely define the acquisition geometry by aligning all the tilt images to a common reference. Errors
introduced in this step will lead to the appearance of artifacts in the tomographic reconstruction, rendering them unsuitable for the sample study. Focusing on
fiducial-based acquisition strategies, this work proposes a deep-learning algorithm to detect misalignment artifacts in tomographic reconstructions by analyzing the
characteristics of these fiducial markers in the tomogram. In addition, we propose an algorithm designed to detect fiducial markers in the tomogram with which to
feed the classification algorithm in case the alignment algorithm does not provide the location of the markers. This open-source software is available as part of the
Xmipp software package inside of the Scipion framework, and also through the command-line in the standalone version of Xmipp.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S1047847723001193

== Related software ==

== Related methods ==

== Comments ==

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