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Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

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Název: Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients
Autoři: Alexander Bigalke, Lasse Hansen, Jasper Diesel, Mattias P. Heinrich
Zdroj: Int J Comput Assist Radiol Surg
Informace o vydavateli: Springer Science and Business Media LLC, 2021.
Rok vydání: 2021
Témata: Machine Learning, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Humans, Original Article, 02 engineering and technology, Cloud Computing, Humans [MeSH], Point clouds, 3D U-Net, Machine Learning [MeSH], Covered patients, Clinical weight estimation, Deep learning, Cloud Computing [MeSH]
Popis: PurposeBody 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.MethodsWe 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.ResultsWe 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$${16}{\%}$$16%and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to$${52}{\%}$$52%.ConclusionWe 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.
Druh dokumentu: Article
Other literature type
Jazyk: English
ISSN: 1861-6429
1861-6410
DOI: 10.1007/s11548-021-02476-0
Přístupová URL adresa: https://link.springer.com/content/pdf/10.1007/s11548-021-02476-0.pdf
https://pubmed.ncbi.nlm.nih.gov/34420184
https://dblp.uni-trier.de/db/journals/cars/cars16.html#BigalkeHDH21
https://europepmc.org/article/MED/34420184
https://link.springer.com/article/10.1007/s11548-021-02476-0
https://doi.org/10.1007/s11548-021-02476-0
https://link.springer.com/content/pdf/10.1007/s11548-021-02476-0.pdf
https://repository.publisso.de/resource/frl:6444513
Rights: CC BY
URL: http://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (http://creativecommons.org/licenses/by/4.0/) .
Přístupové číslo: edsair.doi.dedup.....967ff60ea7daa679cdf946555520b21f
Databáze: OpenAIRE
Popis
Abstrakt:PurposeBody 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.MethodsWe 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.ResultsWe 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$${16}{\%}$$16%and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to$${52}{\%}$$52%.ConclusionWe 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.
ISSN:18616429
18616410
DOI:10.1007/s11548-021-02476-0