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المؤلفAl-Kababji, Ayman Jamal
المؤلفBensaali, Faycal
المؤلفDakua, Sarada Prasad
تاريخ الإتاحة2021-10-18T08:15:38Z
تاريخ النشر2021
اسم المنشورQatar University Annual Research an Exhibition 2021 (quarfe)
الاقتباسAl-Kababji A. J., Bensaali F., Dakua S. P., "Segmenting Liver Volume for Surgical Analysis", Qatar University Annual Research Forum and Exhibition (QUARFE 2021), Doha, 20 October 2021, https://doi.org/10.29117/quarfe.2021.0100
معرّف المصادر الموحدhttps://doi.org/10.29117/quarfe.2021.0100
معرّف المصادر الموحدhttp://hdl.handle.net/10576/24385
الملخصIntroduction: Almost two million people worldwide die annually due to hepatic-related diseases. Half of these diseases are attributed to cirrhosis and the other half are related to hepatitis and hepatocellular carcinoma (HCC). The liver is also a metastasis hub from adjacent organs. This research aims to create an accurate high-quality delineation of the human liver and prepare them to be 3D printed for medical analysis to help aid medical practitioners in pre-procedural planning. Materials and Methods: Convolutional neural networks (ConvNets) are used to perform the liver tissues delineation. A famous ConvNet, named U-net, is used as the basis benchmark architecture that is also known for its great outcomes in the medical segmentation field. Contrast-enhanced computerized tomography (CT) scans are used from the famous Medical Segmentation Decathlon Challenge (Task 8: Hepatic Vessel), abbreviated as MSDC-T8. It contains 443 CT scans, which is considered the largest dataset that contains both the tumors and vessels ground-truth segmentation. Some researchers also generated the liver masks for this dataset, making it a complete dataset that contains all the relevant tissues' ground-truth masks. Results: Currently, the liver delineation has been successfully done with very high DICE = 98.12% (higher than the state-of-the-art results DICE = 97.61%), where a comparison between two famous schedulers namely, ReduceLRonPlateau and OneCycleLR has been conducted. Moreover, the 3D liver volume creation has also been completed and built via the marching cube algorithm. Conclusions/Future Directions: The developed ConvNet can segment livers with high confidence. The tumor(s) and vessels tissues segmentation are also under investigation now. Moreover, newly devised self-organized neural networks (Self-ONN) look promising and will be investigated soon. Lastly, a GUI will be built so that the medical practitioner can just insert the CT volume and get the 3D liver volume with all the segmented tissues.
اللغةen
الناشرQatar University Press
الموضوعLiver delineation
Convolutional neural network
Machine learning
3D model
Surgical planning
العنوانSegmenting Liver Volume for Surgical Analysis
النوعPoster
dc.accessType Open Access


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