Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning

Question Can this deep learning-based method segment soft-tissue tumors faster than can be done manually and more accurately than other automatic methods? Findings The minimally interactive segmentation method achieved accurate segmentation results in internal and external validation, and generalized well across soft-tissue tumor phenotypes and imaging modalities. Clinical relevance This minimally interactive deep learning-based segmentation method could reduce the burden of manual segmentation, facilitate the integration of imaging-based biomarkers (e.g., radiomics) into clinical practice, and provide a fast, semi-automatic solution for volume and diameter measurements (e.g., RECIST).

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Publication Details

Journal
European Radiology
Published
2024-11-19
DOI
https://doi.org/10.1007/s00330-024-11167-8
Citations
5
Primary Topic
Sarcoma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
2.09

Funders

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article

Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning

Cornelis Verhoef, Jacob J. Visser, Wiro J. Niessen, Gonnie C. M. van Erp et al.
5 citations
European Radiology
Sarcoma Diagnosis and Treatment
2.09
article

Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning

Cornelis Verhoef, Jacob J. Visser, Wiro J. Niessen, Gonnie C. M. van Erp, Stefan Klein, David F. Hanff, Dirk J. Grünhagen, Anne‐Rose W. Schut, Judith H. Sluijter, Geert J.L.H. van Leenders, Martijn P. A. Starmans, Douwe J. Spaanderman
article en
5 citations

Abstract

Question Can this deep learning-based method segment soft-tissue tumors faster than can be done manually and more accurately than other automatic methods? Findings The minimally interactive segmentation method achieved accurate segmentation results in internal and external validation, and generalized well across soft-tissue tumor phenotypes and imaging modalities. Clinical relevance This minimally interactive deep learning-based segmentation method could reduce the burden of manual segmentation, facilitate the integration of imaging-based biomarkers (e.g., radiomics) into clinical practice, and provide a fast, semi-automatic solution for volume and diameter measurements (e.g., RECIST).

European RadiologyVol. 35(5)
University Medical Center Groningen (NL), University of Groningen (NL), Erasmus MC (NL), Erasmus MC Cancer Institute (NL), Erasmus University Rotterdam (NL)
European Commission, Hanarth Fonds
Openalex Percentile: Top 12%
Sarcoma Diagnosis and Treatment
2.09
DOIOpen Access PDFOpenAlex
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