{"doi":"10.1101/2024.11.06.622356","title":"TomoScore: A Neural Network Approach for Quality Assessment of Cellular cryoET","abstract":"Electron cryo-tomography (cryo-ET) is a powerful imaging tool that allows three-dimensional visualization of subcellular architecture. During morphological analysis, reliable tomogram segmentation can only be achieved through high-quality data. However, unlike single-particle analysis or subtomogram averaging, the field lacks a useful quantitative measurement of cellular tomogram quality. Currently, the most prevalent method to determine cellular tomogram resolvability is an empirical judgment by experts, which is time-consuming. Methods like FSC between split tilt series suffer from severe geometrical artifacts. We address this gap with a neural network model to predict per-slice resolvability that can apply to tomograms collected from various species and magnifications. We introduce a novel metric, \"TomoScore\", providing a single-value evaluation of cellular tomogram quality, which is a powerful tool for pre-screening tomograms for subsequent automatic segmentation. We further explore the relationship between accumulated electron dose and resulting quality, suggesting an optimum dose range for cryo-ET data collection. Overall, our study streamlines data processing and reduces the need for human involvement during pre-selection for tomogram segmentation.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":505867,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9566,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":743676,"name":"Xueting Zhou","orcid":"0000-0002-0722-7334","position":1,"is_corresponding":false},{"id":1357231,"name":"Ethan Boniuk","orcid":null,"position":2,"is_corresponding":false},{"id":1324332,"name":"Anisha Abraham","orcid":null,"position":3,"is_corresponding":false},{"id":561593,"name":"Zhili Yu","orcid":"0000-0003-2497-7249","position":4,"is_corresponding":false},{"id":233008,"name":"Steven J. Ludtke","orcid":"0000-0002-1903-1574","position":5,"is_corresponding":false},{"id":471321,"name":"Zhao Wang","orcid":"0000-0003-4897-9986","position":7,"is_corresponding":false},{"id":1076793,"name":"Xuqian Tan","orcid":"0000-0001-6044-8876","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:10:50.984927Z","pmid":"39574711","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}