Segmenting Liver Volume for Surgical Analysis
المؤلف | Al-Kababji, Ayman Jamal |
المؤلف | Bensaali, Faycal |
المؤلف | Dakua, Sarada Prasad |
تاريخ الإتاحة | 2021-10-18T08:15:38Z |
تاريخ النشر | 2021 |
اسم المنشور | Qatar University Annual Research an Exhibition 2021 (quarfe) |
الاقتباس | Al-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 |
الملخص | Introduction: 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. |
اللغة | en |
الناشر | Qatar University Press |
الموضوع | Liver delineation Convolutional neural network Machine learning 3D model Surgical planning |
النوع | Poster |
الملفات في هذه التسجيلة
هذه التسجيلة تظهر في المجموعات التالية
-
الهندسة الكهربائية [2611 items ]
-
Theme 2: Health and Biomedical Sciences [80 items ]