Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme

Tuğba Şentürk; Fatma Latifoğlu

doi:10.55007/dufed.1181996

EN TR

Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme

Abstract

Tıbbi görüntüleme alanında derin öğrenme modeli, mevcut yöntemlere kıyasla zaman ve performans açısından daha fazla katkıda bulunan bir modeldir. Görüntülerin otomatik olarak bölütlenmesini veya sınıflandırılmasını kapsar. Mevcut yöntemler ile tek katmanlı görüntüler üzerinden işlem yapılırken, derin öğrenme modeli ile çok katmanlı görüntüler üzerinden çalışma performansı daha yüksek ve daha kesin sonuçlar elde edilebilir. Son zamanlardaki gelişmeler, bu yaklaşımların tıbbi görüntülerdeki örüntülerin tanımlanması ve nicelendirilmesinde oldukça etkili olduğunu göstermektedir. Bu ilerlemelerin en önemli nedeni, derin öğrenme yaklaşımlarının doğrudan görüntülerden hiyerarşik özellik temsilleri elde etme yeteneğidir. Bu nedenle, derin öğrenme yöntemlerinin tıbbi görüntü işleme ve bölütleme alanındaki uygulamaları hızla en son teknolojiye dönüşmektedir ve klinik uygulamalarda performans iyileştirmeleri sağlamaktadır. Bu makalede, derin öğrenme yaklaşımlarının biyomedikal görüntülerin bölütlenmesi için uygulamaları, yöntemleri ve içerikleri genel bir bakış açısıyla incelenmiştir.

Keywords

Görüntüleme, Biyomedikal görüntüler, Derin öğrenme, Biyomedikal görüntü bülütleme, Tıbbi görüntü işleme

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Details

Primary Language

Turkish

Subjects

Engineering

Journal Section

Review

Authors

Tuğba Şentürk ^*
0000-0002-1323-5752
Türkiye

Fatma Latifoğlu
0000-0003-2018-9616
Türkiye

Early Pub Date

June 6, 2023

Publication Date

June 10, 2023

Submission Date

September 29, 2022

Acceptance Date

May 4, 2023

Published in Issue

Year 2023 Volume: 12 Number: 1

DOI

https://doi.org/10.55007/dufed.1181996

IZ

https://izlik.org/JA95BD84NH

APA

Şentürk, T., & Latifoğlu, F. (2023). Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12(1), 161-187. https://doi.org/10.55007/dufed.1181996

AMA

1.Şentürk T, Latifoğlu F. Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme. DUFED. 2023;12(1):161-187. doi:10.55007/dufed.1181996

Chicago

Şentürk, Tuğba, and Fatma Latifoğlu. 2023. “Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme”. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi 12 (1): 161-87. https://doi.org/10.55007/dufed.1181996.

EndNote

Şentürk T, Latifoğlu F (June 1, 2023) Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi 12 1 161–187.

IEEE

[1]T. Şentürk and F. Latifoğlu, “Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme”, DUFED, vol. 12, no. 1, pp. 161–187, June 2023, doi: 10.55007/dufed.1181996.

ISNAD

Şentürk, Tuğba - Latifoğlu, Fatma. “Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme”. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi 12/1 (June 1, 2023): 161-187. https://doi.org/10.55007/dufed.1181996.

JAMA

1.Şentürk T, Latifoğlu F. Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme. DUFED. 2023;12:161–187.

MLA

Şentürk, Tuğba, and Fatma Latifoğlu. “Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme”. Dicle Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 12, no. 1, June 2023, pp. 161-87, doi:10.55007/dufed.1181996.

Vancouver

1.Tuğba Şentürk, Fatma Latifoğlu. Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme. DUFED. 2023 Jun. 1;12(1):161-87. doi:10.55007/dufed.1181996

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Academic Platform Journal of Engineering and Smart Systems

https://doi.org/10.21541/apjess.1508913

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Deep Learning Based Methods for Biomedical Image Segmentation: A Review

Abstract

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Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme

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Comparative Analysis of Baseline Vnet and Unet Architectures on Pancreas Segmentation

Using 3D U-Net for Brain Tumour Segmentation from Magnetic Resonance Images