Segmentasi Lobus Paru Pada CT Scan 2D Menggunakkan Arsitektur U-Net. (Lung Lobe Segmentation On 2D CT Scan Using U-Net Architecture).

Azis, Ahmad Roihan (2024) Segmentasi Lobus Paru Pada CT Scan 2D Menggunakkan Arsitektur U-Net. (Lung Lobe Segmentation On 2D CT Scan Using U-Net Architecture). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

The lung lobe is a crucial part of the human lungs that plays a significant role in respiratory function. Segmenting lung lobes, or separating the structures of lung lobes in medical images, is a critical step in diagnosing lung diseases. Accurate segmentation enables more precise and effective treatment planningThis research aims to evaluate the effect of preprocessing enhancement on lung lobe segmentation using the U-Net architecture. A comparison is made between segmentation results using raw data and data that has undergone preprocessing enhancement. The preprocessing enhancement method employed in this study is contrast stretching enhancement, aimed at improving the contrast and quality of images before segmentation. The dataset used consists of labeled medical images showing the structure of lung lobes.The U-Net architecture is utilized in this study to perform lung lobe segmentation. U-Net is a deep learning architecture commonly used for medical image segmentation tasks due to its effectiveness in handling hierarchical structures.Experimental results indicate that using raw data without preprocessing enhancement yields better model performance, with an accuracy of 10.64% and IoU (Intersection over Union) of 56.11% compared to preprocessed data. The decrease in performance with preprocessing enhancement is attributed to artifacts and noise in medical images, resulting in inaccurate segmentation.

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