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

Share

Top Institutions in Radiology

Brief introduction explaining scope and methodology.

  • #1

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Artificial Intelligence in Medicine

  • #2

    Massachusetts General Hospital / Harvard Medical School
    Massachusetts General Hospital / Harvard Medical School

    Boston, MA

    Key Differentiators

    • Radiology
    • Oncology Imaging
    • Medical AI Research

  • #3

    University of California, Los Angeles (UCLA) Health
    University of California, Los Angeles (UCLA) Health

    Los Angeles, CA

    Key Differentiators

    • Radiology
    • Musculoskeletal Imaging
    • Machine Learning in Medicine

  • #4

    The Netherlands Cancer Institute (NKI)
    The Netherlands Cancer Institute (NKI)

    Amsterdam, N/A

    Key Differentiators

    • Oncology Imaging
    • Radiology
    • Medical Image Analysis

  • #5

    University of Pennsylvania Perelman School of Medicine
    University of Pennsylvania Perelman School of Medicine

    Philadelphia, PA

    Key Differentiators

    • Radiology
    • Biomedical Engineering
    • Artificial Intelligence

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

Related Content

A volume-controlled, anatomy-driven autoplanning strategy for whole-pelvic volumetric modulated arc therapy

A spatial correlation-guided deep fusion framework for multimodal lung cancer classification using CT imaging

Whole-Body MRI: Evidence Gap Widens

Two radiologists argue the for-profit scan industry has outrun the evidence — and the math warrants scrutiny.