Laguna Sonia, Zhang Lin, Bezek Can Deniz, Farkas Monika, Schweizer Dieter, Kubik-Huch Rahel A, Goksel Orcun
Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland.
Department of Information Technology, Uppsala University, Uppsala, Sweden.
Int J Comput Assist Radiol Surg. 2025 Jun 10. doi: 10.1007/s11548-025-03402-4.
Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing SoS images from ultrasound acquisitions can be cast as a limited-angle computed-tomography problem, with variational networks being a promising model-based deep learning solution. Some acquired data frames may, however, get corrupted by noise due to, e.g., motion, lack of contact, and acoustic shadows, which in turn negatively affects the resulting SoS reconstructions.
We propose to use the uncertainty in SoS reconstructions to attribute trust to each individual acquired frame. Given multiple acquisitions, we then use an uncertainty-based automatic selection among these retrospectively, to improve diagnostic decisions. We investigate uncertainty estimation based on Monte Carlo Dropout and Bayesian Variational Inference.
We assess our automatic frame selection method for differential diagnosis of breast cancer, distinguishing between benign fibroadenoma and malignant carcinoma. We evaluate 21 lesions classified as BI-RADS 4, which represents suspicious cases for probable malignancy. The most trustworthy frame among four acquisitions of each lesion was identified using uncertainty-based criteria. Selecting a frame informed by uncertainty achieved an area under curve of 76% and 80% for Monte Carlo Dropout and Bayesian Variational Inference, respectively, superior to any uncertainty-uninformed baselines with the best one achieving 64%.
A novel use of uncertainty estimation is proposed for selecting one of multiple data acquisitions for further processing and decision making.
声速（SoS）是组织的一种生物力学特性，其成像可为诊断提供一种有前景的生物标志物。从超声采集数据重建SoS图像可归结为一个有限角度计算机断层扫描问题，变分网络是一种很有前景的基于模型的深度学习解决方案。然而，一些采集到的数据帧可能会因例如运动、接触不良和声影等原因而被噪声破坏，进而对最终的SoS重建产生负面影响。
我们建议利用SoS重建中的不确定性来赋予每个采集到的单独帧以可信度。给定多个采集数据，我们随后基于不确定性进行回顾性自动选择，以改善诊断决策。我们研究基于蒙特卡洛随机失活和贝叶斯变分推理的不确定性估计。
我们评估了用于乳腺癌鉴别诊断的自动帧选择方法，区分良性纤维腺瘤和恶性癌。我们评估了21个分类为BI-RADS 4级的病变，这代表可能为恶性的可疑病例。使用基于不确定性的标准确定每个病变的四次采集中最可信的帧。对于蒙特卡洛随机失活和贝叶斯变分推理，基于不确定性选择帧分别实现了76%和80%的曲线下面积，优于任何未考虑不确定性的基线方法，最佳基线方法的曲线下面积为64%。
提出了一种利用不确定性估计的新方法，用于从多个数据采集中选择一个进行进一步处理和决策。
