Evidence map›Paper›PMID 42144442›Full record

ArticleEJNMMI physics2026

Multi-modal segment anything model (mmSAM) for tumor segmentation in multi-tracer oncologic PET/CT.

Zhijie Fang, Yibin Liu, Tao Tan, Feng Wang, Greta S P Mok

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhijie FangBiomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
Yibin LiuBiomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.
Tao TanFaculty of Applied Sciences, Macao Polytechnic University, Macau, SAR, China.
Feng WangDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China. fengwangcn@hotmail.com.
Greta S P MokBiomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China. gretamok@um.edu.mo.ORCID http://orcid.org/0000-0002-2962-2087

Funding

Fundo para o Desenvolvimento das Ciências e da Tecnologia 0178/2024/AMJNational Natural Science Foundation of China 82572278
6 · The paper itself

Abstract

purposeLesion segmentation is important in targeted radionuclide therapy dosimetry. In this study, we proposed a novel 2D multi-modal Segment Anything Model (mmSAM) for lesion segmentation in whole-body multi-tracer PET/CT images.

methodsThe AutoPET 2024 dataset, including

resultsmmSAM achieved the best performance as compared to other segmentation methods for the primary

conclusionThe proposed mmSAM is promising for lesion segmentation in multi-tracer oncologic PET/CT images. Fine-tuning significantly enhances segmentation accuracy for cross-tracer studies.

Indexed as

Multi-modalPET/CTSegment anything model (SAM)Tumor segmentation

Identifiers

PMID42144442
PMCPMC13346370

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.