Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23538, Lübeck, Germany.
Drägerwerk AG & Co. KGaA, Moislinger Allee 53-55, 23558, Lübeck, Germany.
Int J Comput Assist Radiol Surg. 2021 Dec;16(12):2079-2087. doi: 10.1007/s11548-021-02476-0. Epub 2021 Aug 21.
Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data.
We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient's volumetric surface without a cover. Second, the patient's weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression.
We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to [Formula: see text] and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to [Formula: see text].
We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.
体重是患者特定治疗的关键参数，特别是在适当药物剂量的情况下。从视觉传感器数据进行非接触式体重估计是克服紧急情况下出现的挑战的一种很有前途的方法。基于机器学习的方法最近已被证明可以从点云数据进行准确的体重估计。然而，所提出的方法是针对患者可见性和位置的特定条件设计的，这限制了它们的实际适用性。在这项工作中，我们旨在通过从体素化点云数据预测覆盖患者的体重来从这些特定条件中解耦准确的体重估计。
我们提出了一种新的深度学习框架，它包括两个 3D CNN 模块，通过两个独立的步骤来解决给定的任务。首先，我们训练一个 3D U-Net 来虚拟地暴露患者，即预测患者没有覆盖物的体积表面。其次，通过我们针对体重回归进行优化的 3D CNN 架构从该 3D 体积预测患者的体重。
我们在两种不同覆盖条件下对躺着姿势数据集（SLP）进行了评估。所提出的框架通过高达[公式：见文本]显著优于基线模型，并通过高达[公式：见文本]缩小了覆盖患者和未覆盖患者体重估计准确性之间的差距。
我们提出了一种新的方法来估计被毯子覆盖的患者的体重。我们的方法放宽了以前非接触式方法要求的准确体重估计的特定条件，因此是迈向临床实践中全自动体重估计的重要一步。
