Newest papers

Image Moment Estimation for Crowded Datasets

Andy Zhang, Oscar Mickelin, Amit Singer · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.22840 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

単一粒子結晶電子顕微鏡(cryo-EM)は、単一粒子結晶電子顕微鏡(cryo-EM)は、単一粒子結晶電子顕微鏡(cryo-EM)の数学モデルは、各画像に正確に一つの粒子が含まれていることを仮定する技術であるが、多くの実験データセットには複数の粒子を含む粒子画像が含まれている:一つの中央粒子といくつかの混雑した粒子。このモデルからの偏差は、低順序の画像の瞬間のような画像統計の不正確な推定につながる。これを考慮するために、我々は、混雑した粒子の効果を修正しながら画像の瞬間を推定するための方法を提案する。この方法は、選択された粒子を含む複数の

Machine translation (Meta M2M100); the English original follows.

Abstract

Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of reconstructing high-resolution three-dimensional (3D) structure of biological macromolecules from a set of noisy projection images taken at random viewing directions. The mathematical model for cryo-EM images typically assumes that each image contains exactly one particle, which is centered at the origin. However, many experimental datasets contain particle images which include multiple particles: one central particle and a few crowding particles. This deviation from the model leads to inaccurate estimation of image statistics, such as the low-order image moments. To account for this, we propose a method to estimate image moments while correcting for the effect of crowding particles. This method can be run directly from a set of cryo-EM micrographs with picked particles, and we demonstrate its efficacy on a wide array of synthetic data.

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Topics: Image (mathematics), Artificial intelligence, Moment (physics), Set (abstract data type), Computer vision, Projection (relational algebra)

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