Publication: Pelvic Tumor Segmentation in Magnetic Resonance Images By U-Net
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Issued Date
2025-01-01
Resource Type
ISSN
21898723
eISSN
30661110
Scopus ID
2-s2.0-105031886072
Journal Title
International Conference on Intelligent Informatics and Biomedical Sciences Iciibms
Start Page
338
End Page
342
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Conference on Intelligent Informatics and Biomedical Sciences Iciibms (2025) , 338-342
Suggested Citation
Nobnop N., Kiatisevi P., Sukjamsri C., Charoenpong T. Pelvic Tumor Segmentation in Magnetic Resonance Images By U-Net. International Conference on Intelligent Informatics and Biomedical Sciences Iciibms (2025) , 338-342. 342. doi:10.1109/ICIIBMS66230.2025.11316723 Retrieved from: https://hdl.handle.net/20.500.14740/55387
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Abstract
Magnetic Resonance Imaging (MRI) is widely used for diagnosing pelvic tumors. Tumor segmentation is an essential step in surgical planning; however, it typically requires specialized experts to manually segment tumors in MRI scans. This research presents a method for segmenting pelvic tumors from MRI images using deep learning technique. A U-Net framework and a U-Net with Batch Normalization architecture for pelvic tumor segmentation are used. To test the performance of the proposed method, we use 24 T2-weighted (T2W) MRI images. The Dice Similarity Coefficient (DSC) and Intersection Over Union (IoU) of the U-Net are 71.99% and 56.24%, respectively, while the DSC and IoU of the U-Net with batch normalization layer are 88.68% and 79.66%, respectively. The U-Net with batch normalization layer shows satisfactory results.