SEGMENTASI PEMBULUH DARAH OTAK DENGAN MENGGUNAKAN METODE V-NET Cerebral Blood Vessel Segmentation Using the V-Net Method

Afriza, Zendi (2025) SEGMENTASI PEMBULUH DARAH OTAK DENGAN MENGGUNAKAN METODE V-NET Cerebral Blood Vessel Segmentation Using the V-Net Method. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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	Text Segmentasi Pembuluh Darah Otak dengan Metode V-net Menggunakan Data Magnetic Resonance Angiogram (MRA).pdf Download (2MB)
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Abstract

Cerebral blood vessel segmentation is a crucial step in medical image analysis, particularly for the diagnosis and treatment planning of neurological conditions such as strokes and aneurysms. This study implements the V-Net method—a deep learning model based on a 3D convolutional neural network—to automatically segment cerebral blood vessels in MRI images. The model is designed to capture complex vascular structural details, including branches and small vessels, with high accuracy. Performance evaluation was conducted using the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics, achieving values ​​of 0.88 and 0.85, respectively. These results demonstrate that the V-Net method provides precise and efficient segmentation. Nevertheless, challenges such as segmenting small vessels and the impact of image noise remain to be addressed. This research is expected to serve as a foundation for further development in medical image analysis applications, specifically regarding cerebral blood vessel segmentation.

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