Skripsi

EGMENTASI INSTANCE MULTICLASS UNTUK INTERPRETASI OBJEK CITRA JANTUNG JANIN

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Penilaian

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This study aims to develop a multiclass instance segmentation model based on Mask RCNN to identify anatomical structures of the normal fetal heart in four-chamber view (4CV) ultrasound images. The background of this research stems from the urgent need for early detection of congenital heart disease (CHD), one of the leading causes of neonatal mortality, combined with the limited availability of fetomaternal subspecialists and the variability in ultrasound image quality encountered in clinical practice. The proposed model is designed as a clinical decision support system powered by artificial intelligence (AI) to assist obstetricians in the initial interpretation of fetal cardiac ultrasound images, particularly in healthcare facilities with limited specialist resources. The dataset used in this study comprises 176 images, extracted from fetal echocardiography videos and annotated into 10 anatomical classes, including major structures (LV, LA, RV, RA, AO), minor structures (PV, MV, TV, AV), and an additional class for the spine, which serves as a reference for fetal anatomical orientation. The study also investigates the impact of various image enhancement techniques, including Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast Limited AHE (CLAHE), Blind Deblurring (BD), and their combinations, on the segmentation performance. Evaluation was conducted using standard performance metrics, namely mean Average Precision (mAP), Intersection over Union (IoU), Dice Similarity Coefficient (DCS), and Confusion Matrix. Experimental results show that the R50_sgd_20 model configuration achieved the best segmentation performance across most major anatomical classes. However, challenges remain in segmenting small anatomical structures such as Pulmonary Valve (PV) and Aortic Valve (AV). Although enhancement techniques improved image clarity, their effect on segmentation accuracy varied across classes. These findings suggest that segmentation performance is influenced not only by image quality, but also by the underlying model architecture, class distribution, and spatial characteristics of the anatomical structures within the image.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507006100	T182190	T1821902025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1821902025

Publisher

Indralaya : Prodi Doktor Ilmu Teknik, Fakultas Teknik Universitas Sriwijaya., 2025

Collation

x, 178 hlm.; ilus.; tab, 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

616.120 7

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

-

Subject(s)

Prodi Doktor Ilmu Teknik
Kelainan Jantung pada Janin

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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