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All Journal Journal of Smart Technology and Engineering
Saragih, Riko
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Evaluasi Segmentasi Otak dan Prediksi Overall Survival pada Dataset BRATS 2020 Fahlevi, Annisa Maizano; Saragih, Riko
Journal of Smart Technology and Engineering Vol. 2 No. 1 (2026)
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jste.v2i1.13880

Abstract

Overall survival (OS) assessment in glioma patients is a crucial component of MRI-based medical image analysis, as OS estimation directly influences clinical decision-making and treatment planning. One of the central challenges in developing image-based predictive models lies in the dependency on accurate tumor segmentation. This study aims to construct an MRI-based OS prediction model using the Brain Tumor Segmentation 2020 (BraTS 2020) dataset by incorporating two types of masked images: ground truth masks and automatically generated predicted masks derived from a 3D U-Net segmentation model. OS classification was grouped into three categories (< 10 months, 10–15 months, and > 15 months). The predictive model achieved an accuracy of 0.9792 when using ground truth masks and 0.9583 when using predicted masks. These findings suggest that a fully automated deep-learning–based segmentation pipeline can approximate the performance of manual segmentation and holds strong potential for large-scale clinical applications where manual annotation is impractical.
Co-Authors Fahlevi, Annisa Maizano