Refining deep learning segmentation in gallium-68-prostate-specific membrane antigen-11 positron emission tomography: evaluation of small lesion filtering and intersection-over-union thresholds

Implementing small lesion filtering criteria improves the reliability of automated segmentation outputs, particularly for quantitative evaluation. For lesion-level metrics, defining true positives within the range of 20-40% IoU thresholds is optimal. Further validation through multicenter studies and larger datasets is essential to ensure the generalizability of these findings.

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Publication Details

Journal
Nuclear Medicine Communications
Published
2026-01-29
DOI
https://doi.org/10.1097/mnm.0000000000002114
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Refining deep learning segmentation in gallium-68-prostate-specific membrane antigen-11 positron emission tomography: evaluation of small lesion filtering and intersection-over-union thresholds

Fou-Ming Liou, Shih-Han Yang, Huang YuYi, Jyh-Cheng Chen et al.
Nuclear Medicine Communications
Medical Imaging Techniques and Applications
article

Refining deep learning segmentation in gallium-68-prostate-specific membrane antigen-11 positron emission tomography: evaluation of small lesion filtering and intersection-over-union thresholds

Fou-Ming Liou, Shih-Han Yang, Huang YuYi, Jyh-Cheng Chen, Yu-Hsin Chen, Yu-Jie Shen
article en

Abstract

Implementing small lesion filtering criteria improves the reliability of automated segmentation outputs, particularly for quantitative evaluation. For lesion-level metrics, defining true positives within the range of 20-40% IoU thresholds is optimal. Further validation through multicenter studies and larger datasets is essential to ensure the generalizability of these findings.

Nuclear Medicine Communications
National Yang Ming Chiao Tung University (TW), China Medical University (TW), Asus (China) (CN), Koo Foundation Sun Yat-Sen Cancer Center (TW), Shandong First Medical University (CN)
Openalex Percentile: Top 74%
Medical Imaging Techniques and Applications
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