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

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

  • By

  • Douwe J. Spaanderman

  • Martijn P. A. Starmans

  • Gonnie C. M. van Erp

  • David F. Hanff

  • Judith H. Sluijter

  • Anne-Rose W. Schut

  • Geert J. L. H. van Leenders

  • Cornelis Verhoef

  • Dirk J. Grünhagen

  • Wiro J. Niessen

  • Jacob J. Visser

  • Stefan Klein

  • November 19, 2024

  • 0 min

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  • 1

    Soft-tissue tumors (STTs) require accurate segmentation for treatment planning and monitoring, but current methods are time-consuming and observer-dependent.

  • 2

    Fully automatic segmentation methods struggle with STTs due to their diverse phenotypes and imaging modalities, limiting their clinical applicability.

  • 3

    A minimally interactive deep-learning method was developed to enhance STT segmentation by incorporating radiologist input for improved accuracy and efficiency.

  • 4

    The study utilized two public datasets for model training and validation, ensuring a comprehensive evaluation of the segmentation methods.

  • 5

    The proposed InteractiveNet method demonstrated improved segmentation performance compared to traditional fully automatic and previous interactive methods.

Original Source(s)

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

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

  • November 19, 2024

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