Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders
Journal:
arXiv
Published Date:
Apr 16, 2025
Abstract
In radiation therapy planning, inaccurate segmentations of organs at risk can
result in suboptimal treatment delivery, if left undetected by the clinician.
To address this challenge, we developed a denoising autoencoder-based method to
detect inaccurate organ segmentations. We applied noise to ground truth organ
segmentations, and the autoencoders were tasked to denoise them. Through the
application of our method to organ segmentations generated on both MR and CT
scans, we demonstrated that the method is independent of imaging modality. By
providing reconstructions, our method offers visual information about
inaccurate regions of the organ segmentations, leading to more explainable
detection of suboptimal segmentations. We compared our method to existing
approaches in the literature and demonstrated that it achieved superior
performance for the majority of organs.
