Skripsi

REKONSTRUKSI CITRA ULTRASONOGRAFI TRANSCEREBELLAR 2 DIMENSI MENJADI TRANSCEREBELLAR 3 DIMENSI MENGGUNAKAN METODE 3D-FHNet

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Penilaian

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Reconstruction of two-dimensional transcerebellar images into three�dimensional transcerebellar has an important role in diagnosis and treatment planning in neurology. This study proposes the 3D-FHNet method to produce an accurate three-dimensional representation of transcerebellar images. The study began with segmentation of transcerebellar objects on well-labeled ultrasound datasets. The segmentation architecture used is U-Net and LinkNet as a comparison. After training, both architectures can process object segmentation properly. The best performance was produced by U-Net with a pixel accuracy of 99.83%, Mean IU of 89.71%, FPR of 0.91%, Precision of 85.78%, Recall of 85.31%, and F1 Score of 85.31%. With this accuracy, cross-validation was carried out using a 10-Fold K-Fold. The U-Net architecture was chosen to continue the 3D reconstruction process. The reconstruction process was carried out using 2 models as an improvement on the previous results using the PiFUHD method. The PiFUHD method succeeded in creating a 3-dimensional representation with one input from the previous segmentation results. From the results of the 3-dimensional representation, 4 sides are taken and then implemented into the 3D-FHNet method. The resulting object is the same as a 3-dimensional object. Model performance can be calculated by using the input as ground truth or the IoU (Intersection over Union) method with an average of 76.76% for 19 test data. By using the 3D-FHNet method, three-dimensional image reconstruction of the ultrasound image of the fetal head can be performed accurately.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307005116	T124965	T1249652023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1249652023

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xvi, 61 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jurusan Sistem Komputer
Jaringan Komunikasi Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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