[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118977-en":3,"doc-seo-118977-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118977,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Fully Automated Explainable Abdominal CT Contrast Media Phase Classification Using Organ Segmentation and Machine Learning","Contrast-enhanced computed tomography (CECT) provides substantially more diagnostic information than non-enhanced CT, yet injection-phase labels are often absent from public datasets and are inconsistently recorded in routine clinical practice. This study develops a fully automated approach to infer contrast media injection phase directly from CT images using organ segmentation with deep learning combined with machine learning based feature extraction. A dataset of 2509 scans from two CT systems is classified into noncontrast, arterial, venous, and delayed phases using 10-fold evaluation, yielding near-perfect performance.","Received: 28 December 2023 Revised: 19 March 2024 Accepted: 2 April 2024  \nDOI: 10.1002/mp.17076  \nRESEARCH ARTICLE  \nFully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning  \nYazdan Salimi1  Zahra Mansouri1  Ghasem Hajianfar1  Amirhossein Sanaat1  Isaac Shiri1,2  Habib Zaidi1,3,4,5  \n1 Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland  \n2 Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland  \n3 Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands  \n4 Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark  \n5 University Research and Innovation Center,Óbuda University, Budapest, Hungary  \nCorrespondence  \nDivision of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.  \nEmail: [habib.zaidi@hcuge.ch](habib.zaidi@hcuge.ch)  \nFunding information  \nEuratom research and training programme 2019−2020 Sinfonia project, Grant/Award Number: 945196  \nAbstract  \nBackground: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research.  \nPurpose: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms. Methods: A total number of 2509 CT images split into four subsets of noncontrast (class \\#0), arterial (class \\#1), venous (class \\#2), and delayed (class \\#3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation,10,50,and 90 percentiles extracted from the abovementioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics.  \nResults: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0 .9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class \\#2 (0 .9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼ 1.4%) were misclassified. The performance over all folds was consistent.  \nConclusions: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.  \nKEYWORDS  \ncontrast-enhanced CT, data curation, deep learning, machine learning, segmentation  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.  \n2  \nFULLY AUTOMATED AB","cbCaigxMl8FyQ6im","https://ap.wps.com/l/cbCaigxMl8FyQ6im","pdf",4145553,1,10,"English","en",105,"# Abstract\n## Background\n## Purpose\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Clinical role of contrast-enhanced CT\n## Motivation for automated phase inference","[{\"question\":\"Why is injection phase information difficult to use in clinical research with CT data?\",\"answer\":\"Injection-phase information is frequently missing in public datasets and may not be standardized across clinical sites, limiting effective reuse of CECT images for research.\"},{\"question\":\"How does the proposed method determine the contrast media injection phase?\",\"answer\":\"It uses deep learning to segment seven organs and body contour masks, extracts first-order statistical features from the segmented regions, and applies machine learning with feature selection and reduction to classify the phase into four classes.\"},{\"question\":\"What classification performance was achieved and how was it evaluated?\",\"answer\":\"Using Boruta feature selection with a random forest model, the method reached an average AUC above 0.999 and accuracy around 0.9936 across four classes over 10 folds. Misclassifications were low, with only 33 cases out of 2509.\"}]","Fully Automated Explainable Abdominal CT Contrast Media Phase Classification Using Organ Segmentation and Machine Learning | PDF",1785721302,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fully-automated-explainable-abdominal-ct-contrast-media-phase-classification-using-organ-segmentation-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fully-automated-explainable-abdominal-ct-contrast-media-phase-classification-using-organ-segmentation-and-machine-learning/118977/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is injection phase information difficult to use in clinical research with CT data?","Question",{"text":75,"@type":76},"Injection-phase information is frequently missing in public datasets and may not be standardized across clinical sites, limiting effective reuse of CECT images for research.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method determine the contrast media injection phase?",{"text":80,"@type":76},"It uses deep learning to segment seven organs and body contour masks, extracts first-order statistical features from the segmented regions, and applies machine learning with feature selection and reduction to classify the phase into four classes.",{"name":82,"@type":73,"acceptedAnswer":83},"What classification performance was achieved and how was it evaluated?",{"text":84,"@type":76},"Using Boruta feature selection with a random forest model, the method reached an average AUC above 0.999 and accuracy around 0.9936 across four classes over 10 folds. Misclassifications were low, with only 33 cases out of 2509.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]