Can I read Can Ultrasound Confidence Maps Predict Sonographers' Labeling Variability? on EtoBox?

Can Ultrasound Confidence Maps Predict Sonographers' Labeling Variability? by Duque, Vanessa Gonzalez; Zirus, Leonhard; Velikova, Yordanka; Navab, Nassir; Mateus, Diana is a scholarly article available to read on EtoBox.

What is Can Ultrasound Confidence Maps Predict Sonographers' Labeling Variability? about?

Measuring cross-sectional areas in ultrasound images is a standard tool to evaluate disease progress or treatment response. Often addressed today with supervised deep-learning segmentation approaches, existing solutions highly depend upon the quality of experts' annotations. However, the annotation quality in ultrasound is anisotropic and position-variant due to the inherent physical imaging principles, including attenuation, shadows, and missing boundaries, commonly exacerbated with depth. This work proposes a novel approach that guides ultrasound segmentation networks to account for sonographers' uncertainties and generate predictions with variability similar to the experts. We claim that realistic variability can reduce overconfident predictions and improve physicians' acceptance of deep-learning cross-sectional segmentation solutions. Our method provides CM's certainty for each pixel for minimal computational overhead as it can be precalculated directly from the image. We show that there is a correlation between low values in the confidence maps and expert's label uncertainty. Therefore, we propose to give the confidence maps as additional information to the networks. We study

Author

Duque, Vanessa Gonzalez; Zirus, Leonhard; Velikova, Yordanka; Navab, Nassir; Mateus, Diana

Published

2023

Language

EN