{"doi":"10.1117/1.jmi.12.5.057501","title":"Fine-grained multiclass nuclei segmentation with molecular empowered all-in-SAM model","abstract":"Purpose: Recent developments in computational pathology have been driven by advances in vision foundation models (VFMs), particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the use of cell-specific SAM models for direct segmentation. These approaches enable effective segmentation across a range of nuclei and cells. However, general VFMs often face challenges with fine-grained semantic segmentation, such as identifying specific nuclei subtypes or particular cells. Approach: In this paper, we propose the molecular empowered all-in-SAM model to advance computational pathology by leveraging the capabilities of VFMs. This model incorporates a full-stack approach, focusing on (1) annotation-engaging lay annotators through molecular empowered learning to reduce the need for detailed pixel-level annotations, (2) learning-adapting the SAM model to emphasize specific semantics, which utilizes its strong generalizability with SAM adapter, and (3) refinement-enhancing segmentation accuracy by integrating molecular oriented corrective learning. Results: Experimental results from both in-house and public datasets show that the all-in-SAM model significantly improves cell classification performance, even when faced with varying annotation quality. Conclusions: Our approach not only reduces the workload for annotators but also extends the accessibility of precise biomedical image analysis to resource-limited settings, thereby advancing medical diagnostics and automating pathology image analysis.","journal":"Journal of Medical Imaging","year":2025,"id":574135,"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":0.9626,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1037188,"name":"Can Cui","orcid":"0000-0002-2159-5387","position":1,"is_corresponding":false},{"id":643160,"name":"Ruining Deng","orcid":"0000-0001-6300-8518","position":2,"is_corresponding":false},{"id":425889,"name":"Yucheng Tang","orcid":"0000-0002-6008-9700","position":3,"is_corresponding":false},{"id":311633,"name":"Quan Liu","orcid":"0000-0001-9746-2938","position":4,"is_corresponding":false},{"id":978911,"name":"Tianyuan Yao","orcid":"0000-0002-1848-079X","position":5,"is_corresponding":false},{"id":425888,"name":"Shunxing Bao","orcid":"0000-0001-6376-4292","position":6,"is_corresponding":false},{"id":343813,"name":"Naweed I. Chowdhury","orcid":"0000-0001-9826-4917","position":7,"is_corresponding":false},{"id":340022,"name":"Haichun Yang","orcid":"0000-0003-4265-7492","position":8,"is_corresponding":false},{"id":291228,"name":"Yuankai Huo","orcid":"0000-0002-2096-8065","position":9,"is_corresponding":false},{"id":1330988,"name":"Xueyuan Li","orcid":"0000-0002-0903-9388","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:57:40.686992Z","pmid":"40918610","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":[]}