[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128565-en":3,"doc-seo-128565-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128565,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine Learning Approaches For Slice To Volume Biomedical Data Integration - Doctor of Philosophy Thesis","Imaging underpins modern biomedical research and clinical diagnosis. This thesis focuses on slice-to-volume registration, treating 2D-to-3D slice-to-volume mapping as a key problem for integrating new experimental data into biomedical atlases. Manual positioning of 2D sections into 3D models is costly and expert-dependent. To automate the workflow, two datasets are constructed by converting a 3D atlas into 2D slices, and a convolutional neural network is trained to predict slice pose parameters, achieving strong accuracy. Experiments evaluate multiple CNN variants and transfer learning strategies across all available atlas modalities, including image and textual anatomical data, with results that outperform image-only baselines on unseen datasets.","Machine Learning Approaches For Slice To Volume Biomedical Data Integration  \nby  \nBassam Almogadwy  \nSubmitted for the degree of Doctor of Philosophy  \nDepartment of Computer Science  \nSchool of Mathematical and Computer Sciences  \nHeriot-Watt University  \nSeptember 2023  \nThe copyright in this thesis is owned by the author. Any quotation from the thesis or use of any of the information contained in it must acknowledge this thesis as the source of the quotation or information.  \nAbstract  \nImaging plays an essential role in modern biomedical sciences and lays the foundation for current research and clinical diagnosis. During the last decade, slice-tovolume registration, a particular type of image registration problem, has received great attention from the medical imaging community due to the emergence of several medical applications of slice-to-volume mapping (2D to 3D image mapping) using biomedical data such as biomedical atlas. The task of integrating new data into a biomedical atlas is a typical 2D to 3D image registration problem. Images created in experiments are mostly 2D images, while modern biomedical atlases are mostly 3D models. To transfer the data related to the 2D image (e.g., gene expression data) to the 3D Atlas, it is necessary to determine the position of the new image in the 3D model. This is typically done by experts who review the 2D sections and manually position 2D data into 3D with some tools. Manual positioning 2D data into 3D is financially expensive, time consuming, and require extensive work by experts. However, finding experts who have domain knowledge is also another crucial challenge. To resolve this problem, this thesis automate the process of positioning the 2D image into the 3D model. This study contributes by creating two datasets that convert the 3D Atlas into a series of 2D slices. Then, we utilize a Convolutional Neural Network (CNN) for registering purposes. The proposed CNN model is trained to determine the distance and pitch values used to describe the position of the 2D slice in the atlas coordinate system, and the proposed model obtained 94% accuracy. Furthermore, we tested different variants of CNN architectures and different transfer learning techniques to build an optimal image base model for image analysis. We employ all the data modalities available in the biomedical Atlases, such as the images and the textual anatomical data. To test the performance in real-life situation, the performance of the proposed model is evaluated on the unseen dataset. The results show that the proposed model outperforms the image-only data and obtain 97% accuracy. A different data set (contained cropped images) is used to test the performance of the proposed technique for image matching, and the algorithm achieved 94% accuracy. The study has shown that different data modalities available within the atlases can train the machine learning to overcome many of the issues related to the use of image-processing based or ontology-based techniques.  \nAcknowledgements  \nIn the name of Allah the most graceful and gracious, I would like to thank Allah (God) for everything because without his help and guidance, all this wouldn’t have been possible. Many prayers upon the prophet Muhammad peace be upon him who said, “who does not thank people does not thank Allah”. I want to express my sincere gratitude to my supervisor Professor Albert Burger for his continuing and endless support during my PhD and for his insightful comments and invaluable guidance. Without his encouragement and patience it would not have been possible to conduct this research.Special thanks go to my second supervisor Professor Nick Taylor for help and support he has offered throughout my course of research.  \nMany great thanks to my family, especially my father and mother for their patience during studying abroad, also for their deep love and for everything they have done so far which have supported me not only in this research but in all my li","cbCairhepIZBi62S","https://ap.wps.com/l/cbCairhepIZBi62S","pdf",4437013,3,1,141,"English","en",105,"# Abstract\n# Acknowledgements\n# Inclusion of Published Works Form\n# Declaration","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses slice-to-volume registration for integrating new biomedical data into a 3D biomedical atlas by mapping 2D images to 3D coordinates.\"},{\"question\":\"How does the proposed method reduce expert effort?\",\"answer\":\"It automates the positioning of 2D slices in the 3D model by training a convolutional neural network to predict pose parameters in the atlas coordinate system.\"},{\"question\":\"What data and model variants are used to evaluate performance?\",\"answer\":\"The study uses multiple available atlas modalities, including image data and textual anatomical data, and tests different CNN architecture variants and transfer learning techniques on unseen datasets.\"}]","Machine Learning Approaches For Slice To Volume Biomedical Data Integration - Doctor of Philosophy Thesis | PDF",1786001768,355,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-approaches-for-slice-to-volume-biomedical-data-integration-doctor-of-philosophy-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-slice-to-volume-biomedical-data-integration-doctor-of-philosophy-thesis/128565/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address?","Question",{"text":76,"@type":77},"It addresses slice-to-volume registration for integrating new biomedical data into a 3D biomedical atlas by mapping 2D images to 3D coordinates.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method reduce expert effort?",{"text":81,"@type":77},"It automates the positioning of 2D slices in the 3D model by training a convolutional neural network to predict pose parameters in the atlas coordinate system.",{"name":83,"@type":74,"acceptedAnswer":84},"What data and model variants are used to evaluate performance?",{"text":85,"@type":77},"The study uses multiple available atlas modalities, including image data and textual anatomical data, and tests different CNN architecture variants and transfer learning techniques on unseen datasets.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]