Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.
Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the performance of TotalSegmentator, a state-of-the-art adult-trained CT organ segmentation model, on a subset of organs in a pediatric CT dataset and expl...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1628 - 1642 |
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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=185280516&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280516 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: 185280516 185280516 185280516 10.1007/s10278-024-01273-w 185280516 ppf: 1628 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population. aug: au: Chatterjee, Devina Kanhere, Adway Doo, Florence X. Zhao, Jerry Chan, Andrew Welsh, Alexander Kulkarni, Pranav Trang, Annie Parekh, Vishwa S. Yi, Paul H. affil: https://ror.org/055yg0521 Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA sug: subj: Radiography, Abdominal Methods Tomography, X-Ray Computed Methods Deep Learning Image Processing, Computer Assisted Methods Prediction Models Evaluation Predictive Value of Tests Evaluation Human Funding Source Child Retrospective Design Record Review Descriptive Statistics Mann-Whitney U Test Confidence Intervals Wilcoxon Rank Sum Test Data Analysis Software Imaging, Three-Dimensional Adrenal Glands Radiography Duodenum Radiography Pancreas Radiography Age Factors Child: 6-12 years ab: Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the performance of TotalSegmentator, a state-of-the-art adult-trained CT organ segmentation model, on a subset of organs in a pediatric CT dataset and explored optimization strategies to improve pediatric segmentation performance. TotalSegmentator was retrospectively evaluated on abdominal CT scans from an external adult dataset (n = 300) and an external pediatric data set (n = 359). Generalizability was quantified by comparing Dice scores between adult and pediatric external data sets using Mann–Whitney U tests. Two DL optimization approaches were then evaluated: (1) 3D nnU-Net model trained on only pediatric data, and (2) an adult nnU-Net model fine-tuned on the pediatric cases. Our results show TotalSegmentator had significantly lower overall mean Dice scores on pediatric vs. adult CT scans (0.73 vs. 0.81, P <.001) demonstrating limited generalizability to pediatric CT scans. Stratified by organ, there was lower mean pediatric Dice score for four organs (P <.001, all): right and left adrenal glands (right adrenal, 0.41 [0.39–0.43] vs. 0.69 [0.66–0.71]; left adrenal, 0.35 [0.32–0.37] vs. 0.68 [0.65–0.71]); duodenum (0.47 [0.45–0.49] vs. 0.67 [0.64–0.69]); and pancreas (0.73 [0.72–0.74] vs. 0.79 [0.77–0.81]). Performance on pediatric CT scans improved by developing pediatric-specific models and fine-tuning an adult-trained model on pediatric images where both methods significantly improved segmentation accuracy over TotalSegmentator for all organs, especially for smaller anatomical structures (e.g., > 0.2 higher mean Dice for adrenal glands; P <.001). pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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