Segmentasi Cerebrum Pada Citra MRI Untuk Perhitungan Volume Menggunakan U-Net

Jayani, Annafi Nur (2025) Segmentasi Cerebrum Pada Citra MRI Untuk Perhitungan Volume Menggunakan U-Net. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perubahan volume gray matter dan white matter pada bagian cerebrum merupakan indikator penting dalam studi penuaan otak dan deteksi dini penyakit neurodegeneratif. Segmentasi citra MRI otak yang akurat diperlukan untuk memperoleh estimasi volume jaringan otak secara presisi. Penelitian ini mengusulkan pendekatan berbasis Convolutional Neural Networks (CNN) menggunakan arsitektur U-Net untuk melakukan segmentasi otomatis pada citra MRI otak dan menghitung volume gray matter serta white matter. Dataset MRI publik digunakan dengan fokus segmentasi pada bagian cerebrum. Pendekatan ini diharapkan dapat meningkatkan akurasi dan efisiensi segmentasi dibandingkan metode tradisional, serta mendukung pemantauan kondisi neurologis berbasis perubahan volume otak.
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Changes in gray matter and white matter volumes in the cerebrum are important indicators in brain aging studies and early detection of neurodegenerative diseases. Accurate brain MRI image segmentation is required for precise estimation of brain tissue volume. This study proposes a Convolutional Neural Network (CNN)-based approach using the U-Net architecture to automatically segment brain MRI images and calculate the volume of gray matter and white matter. A public brain MRI dataset is used, focusing on the cerebrum region. The proposed method is expected to improve segmentation accuracy and efficiency compared to traditional methods, and to support neurological monitoring based on changes in brain volume.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Segmentasi Otak,Deep Learning,CNN,Volume Cerebrum,Unet,MRI, Brain Segmentation,Deep Learning,CNN,Cerebrum Volume,Unet,MRI
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models. T Technology > T Technology (General) > T58.64 Information resources management
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis
Depositing User:	Annafi Nur Jayani
Date Deposited:	29 Jul 2025 07:40
Last Modified:	29 Jul 2025 07:40
URI:	http://repository.its.ac.id/id/eprint/123244

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