Mandibular Image Segmentation and 3d Reconstruction using U-Net Model
DOI:
https://doi.org/10.12962/j24068535.v23i1.a1245Abstract
Penelitian ini bertujuan untuk meningkatkan presisi dan efisiensi dalam segmentasi citra mandibula dan rekonstruksi 3D menggunakan model U-Net. Segmentasi otomatis dengan U-Net menangani tantangan metode manual yang memakan waktu. Struktur Encoder-Decoder pada U-Net memungkinkan pembelajaran fitur citra medis yang kompleks dengan akurasi tinggi, menghasilkan segmentasi yang konsisten dan presisi. Hasil penelitian menunjukkan bahwa Res U-Net mencapai performa segmentasi yang unggul dengan Dice Similarity Coefficient (DSC) sebesar 95,37%, meskipun memerlukan waktu komputasi yang lebih lama. Sementara itu, U-Net standar menawarkan efisiensi komputasi yang lebih tinggi dan cocok untuk aplikasi real-time meskipun akurasinya sedikit lebih rendah. Integrasi segmentasi dengan rekonstruksi 3D meningkatkan visualisasi anatomi mandibula, memperbaiki efektivitas perencanaan bedah, serta menyediakan alat simulasi interaktif untuk perawatan personal dan pelatihan profesional. Penggunaan standar DICOM memfasilitasi aksesibilitas antar perangkat medis, mendukung interoperabilitas sistem perawatan kesehatan. Studi ini menyimpulkan bahwa Res U-Net optimal untuk kebutuhan presisi tinggi, sedangkan U-Net lebih cocok untuk aplikasi dengan pemrosesan cepat. Temuan ini diharapkan dapat memajukan teknologi segmentasi dan visualisasi medis yang andal dan efektif dalam praktik klinis.References
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