ProPicker: Promptable Segmentation for Particle Picking in Cryogenic Electron Tomography

Simon Wiedemann, Zalan Fabian, Mahdi Soltanolkotabi, Reinhard Heckel

Keywords: cryo-ET, cryo-EM, cryogenic electron tomography, cryogenic electron microscopy, particle, picking, particle picking, object detection

Abstract:

Cryogenic electron tomography (cryo-ET) can produce detailed 3D images called
tomograms of cellular environments. An essential step of cryo-ET reconstruction and analysis is to find all instances of a protein in tomograms, a task known as particle picking. Due to the low signal-to-noise ratio, artifacts, and vast diversity in proteins, particle picking is a challenging 3D object detection problem. Existing approaches are either slow or limited to picking a few particles of interest, which requires large annotated and difficult to obtain training datasets. In this work, we propose ProPicker, a fast and universal particle picker that can detect particles beyond those in the training set. Our promptable design allows for selectively detecting a specific protein in the volume based on an input prompt. Our experiments demonstrate that through a favorable trade-off between performance and speed, ProPicker can achieve performance close to or on par with state-of-the-art universal pickers, while being up to an order of magnitude faster. Moreover, ProPicker can be efficiently adapted to new proteins through fine-tuning on few annotated samples.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 10249