Deep learning based liver segmentation using T1 weighted abdominal MRI
Md Sakib Abrar Hossain1, Muhammad E. H. Chowdhury1, Enamul H. Bhuyian2, Tawsifur Rahman3, Zaid B. Mahbub4, Amith Khandakar3, Anas Tahir3, Md Shafayet Hossain5, and M. Salman Khan6
1Electrical Engineering, Qatar University, Doha, Qatar, 2Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 3Electrical Engineering,, Qatar University, Doha, Qatar, 4Dept. of Physics and Mathematics, North South University, Dhaka, Bangladesh, 5Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Malaysia, 6Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan
MR scans are preferred by clinicians for liver pathology diagnosis over volumetric abdominal CT scans, due to their superior resolution for soft tissues. Nevertheless, deep learning based automated liver segmentation from abdominal MRI is challenging as the liver exhibits variable characteristics. This study investigates multiple state-of-the-art segmentation architectures (UNet, UNet++, and FPN) with varying encoder and decoder backbones. Here, T1 weighted MR images are investigated as it demonstrates brighter fat content. Among the investigated networks UNet++ with DenseNet backbone demonstrates top performance for the liver segmentation with a DSC and IoU of 94.3% and 91.0%, respectively.
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