Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy.

Caroline Elisabeth Olsson^1,2, Rahul Suresh¹, Jarkko Niemelä³, Saad Ullah Akram³, Alexander Valdman⁴

¹Medical Radiation Sciences, Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.

²Regional Cancer Centre West, Western Sweden Healthcare Region, Gothenburg, Sweden.

³MVision AI Oy, Helsinki, Finland.

⁴Department of Radiotherapy, Karolinska University Hospital, Stockholm, Sweden.

PubMed PMC DOI PDF

Abstract

BACKGROUND AND PURPOSE: Autosegmentation techniques are emerging as time-saving means for radiation therapy (RT) contouring, but the understanding of their performance on different datasets is limited. The aim of this study was to determine agreement between rectal volumes by an existing autosegmentation algorithm and manually-delineated rectal volumes in prostate cancer RT. We also investigated c…

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