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 the effect of the proposed use of ultrasound CMs in combination with four state-of-the-art neural networks and in two configurations: as a second input channel and as part of the loss. We evaluate our method on 3D ultrasound datasets of the thyroid and lower limb muscles. Our results show ultrasound CMs increase the Dice score, improve the Hausdorff and Average Surface Distances, and decrease the number of isolated pixel predictions. Furthermore, our findings suggest that ultrasound CMs improve the penalization of uncertain areas in the ground truth data, thereby improving problematic interpolations. Our code and example data will be made public at https://github.com/IFL-CAMP/Confidence-segmentation.

翻译：在超声图像中测量横截面积是评估疾病进展或治疗反应的标准工具。如今常通过有监督的深度学习分割方法来解决，现有解决方案高度依赖于专家标注的质量。然而，由于超声固有的物理成像原理（包括衰减、阴影和边界缺失，且随深度增加而加剧），其标注质量呈各向异性和位置依赖性。本文提出一种新方法，引导超声分割网络考虑超声医师的标注不确定性，并生成与专家标注变异性相似的预测结果。我们认为，真实的变异性可以减少过度自信的预测，提升临床医生对基于深度学习的横截面分割方案的接受度。本方法可为每个像素提供置信图的确定性，且计算开销极低，因为置信图可直接从图像中预先计算得出。我们证明，置信图中的低值与专家的标签不确定性之间存在相关性。因此，我们建议将置信图作为额外信息输入网络。我们研究了在两种配置下（作为第二输入通道和作为损失函数的一部分）与四种先进神经网络结合时超声置信图的效果。我们使用甲状腺和下肢肌肉的三维超声数据集评估了该方法。结果表明，超声置信图能提升Dice系数、改善豪斯多夫距离和平均表面距离，并减少孤立像素预测的数量。此外，我们的发现表明，超声置信图增强了对真实数据中不确定区域的惩罚，从而改善了有问题的插值处理。我们的代码和示例数据将在https://github.com/IFL-CAMP/Confidence-segmentation公开。