Cross-site Validation of AI Segmentation and Harmonization in Breast MRI.
This work aims to perform a cross-site validation of automated segmentation for breast cancers in MRI and to compare the performance to radiologists. A three-dimensional (3D) U-Net was trained to segment cancers in dynamic contrast-enhanced axial MRIs using a large dataset from Site 1 (n = 15,266; 4...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1642 - 1653 |
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| Autores principales: | , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280511&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280511 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280511 185280511 189894284 185280511 10.1007/s10278-024-01266-9 185280511 ppf: 1642 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cross-site Validation of AI Segmentation and Harmonization in Breast MRI. aug: au: Huang, Yu Leotta, Nicholas J. Hirsch, Lukas Gullo, Roberto Lo Hughes, Mary Reiner, Jeffrey Saphier, Nicole B. Myers, Kelly S. Panigrahi, Babita Ambinder, Emily Di Carlo, Philip Grimm, Lars J. Lowell, Dorothy Yoon, Sora Ghate, Sujata V. Parra, Lucas C. Sutton, Elizabeth J. affil: https://ror.org/00wmhkr98 Department of Biomedical Engineering, The City College of the City University of New York, 160 Convent Ave, 10031, New York, NY, USA sug: subj: Breast Neoplasms Diagnosis Magnetic Resonance Imaging Automation Artificial Intelligence Validity Evaluation Radiologists Deep Learning Image Enhancement Image Interpretation, Computer Assisted Human Female United States Retrospective Design Comparative Studies Nonparametric Statistics Descriptive Statistics Imaging, Three-Dimensional Funding Source Female ab: This work aims to perform a cross-site validation of automated segmentation for breast cancers in MRI and to compare the performance to radiologists. A three-dimensional (3D) U-Net was trained to segment cancers in dynamic contrast-enhanced axial MRIs using a large dataset from Site 1 (n = 15,266; 449 malignant and 14,817 benign). Performance was validated on site-specific test data from this and two additional sites, and common publicly available testing data. Four radiologists from each of the three clinical sites provided two-dimensional (2D) segmentations as ground truth. Segmentation performance did not differ between the network and radiologists on the test data from Sites 1 and 2 or the common public data (median Dice score Site 1, network 0.86 vs. radiologist 0.85, n = 114; Site 2, 0.91 vs. 0.91, n = 50; common: 0.93 vs. 0.90). For Site 3, an affine input layer was fine-tuned using segmentation labels, resulting in comparable performance between the network and radiologist (0.88 vs. 0.89, n = 42). Radiologist performance differed on the common test data, and the network numerically outperformed 11 of the 12 radiologists (median Dice: 0.85–0.94, n = 20). In conclusion, a deep network with a novel supervised harmonization technique matches radiologists' performance in MRI tumor segmentation across clinical sites. We make code and weights publicly available to promote reproducible AI in radiology. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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