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Body Part Regression Model for CT Volumes

Authors: Schuhegger, Sarah;

Body Part Regression Model for CT Volumes

Abstract

{"references": ["Ke Yan, Le Lu, and Ronald M Summers. \"Unsupervised body part regression via spatiallyself-ordering convolutional neural networks\". In:2018 IEEE 15th International Symposiumon Biomedical Imaging (ISBI 2018). IEEE. 2018, pp. 1022\u20131025.", "Amber L Simpson et al. \"A large annotated medical image dataset for the development andevaluation of segmentation algorithms\". In:arXiv preprint arXiv:1902.09063(2019)", "Patrick Bilic et al. \"The liver tumor segmentation benchmark (lits)\". In:arXiv preprintarXiv:1901.04056(2019).", "Marc A Attiyeh et al. \"Survival prediction in pancreatic ductal adenocarcinoma by quanti-tative computed tomography image analysis\". In:Annals of surgical oncology25.4 (2018),pp. 1034\u20131042", "Marc A Attiyeh et al. \"Preoperative risk prediction for intraductal papillary mucinous neo-plasms by quantitative CT image analysis\". 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In:Microbi-ology Resource Announcements10.1 (2021)", "Shivang Desai et al. \"Chest imaging representing a COVID-19 positive rural US population\".In:Scientific data7.1 (2020), pp. 1\u20136", "Baghal A Desai S et al.Data from Chest Imaging with Clinical and Genomic CorrelatesRepresenting a Rural COVID-19 Positive Population. 2020.doi:https://doi.org/10.7937/tcia.2020.py71-5978", "Lu L Roth H et al.A new 2.5 D representation for lymph node detection in CT [Data set].2015.doi:https://doi.org/10.7937/K9/TCIA.2015.AQIIDCNM", "Holger R Roth et al. \"A new 2.5 D representation for lymph node detection using random setsof deep convolutional neural network observations\". In:International conference on medicalimage computing and computer-assisted intervention. Springer. 2014, pp. 520\u2013527", "Ari Seff et al. \"2D view aggregation for lymph node detection using a shallow hierarchy oflinear classifiers\". 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In:Medical physics38.2 (2011), pp. 915\u2013931.", "Elhalawani H Grossberg A et al.Cancer Center Head and Neck Quantitative Imaging WorkingGroup (HNSCC). 2020.doi:https://doi.org/10.7937/k9/tcia.2020.a8sh-7363", "Aaron J Grossberg et al. \"Imaging and clinical data archive for head and neck squamous cellcarcinoma patients treated with radiotherapy\". In:Scientific data5.1 (2018), pp. 1\u201310", "Hesham Elhalawani et al. \"Matched computed tomography segmentation and demographicdata for oropharyngeal cancer radiomics challenges\". In:Scientific data4 (2017), p. 170077.", "Ulrich E J Beichel R R et al.Data from QIN-HEADNECK [Data set]. 2015.doi:10.7937/K9/TCIA.2015.K0F5CGLI", "Andriy Fedorov et al. \"DICOM for quantitative imaging biomarker development: a standardsbased approach to sharing clinical data and structured PET/CT analysis results in head andneck cancer research\". 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The model can be used to estimate the examined body parts from a CT volume. The model was trained on several publicly available datasets. It corresponds to the Python package bpreg. If you want to use this model, please make sure to cite the training data as well (summarized in the reference.xlsx file).

Keywords

Body Part Examined, CT volumes, body part recognition, body part regression

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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