Deep Learning Based Methods for Biomedical Image Segmentation: A Review

Year 2023, , 161 - 187, 10.06.2023

Tuğba Şentürk , Fatma Latifoğlu

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

Cited By: 2

Abstract

A deep learning model is a model in the field of medical imaging that provides more contributions in terms of time and performance compared to existing methods. It includes automatic segmentation or classification of images. While existing methods process single-layer images, with the deep learning model, higher performance and more accurate results can be obtained on multi-layer images. Recent developments show that these approaches are highly effective in identifying and quantifying patterns in medical images. The most important reason for these advances is the core function of deep learning approaches to directly obtain hierarchical feature representations from images. Therefore, the applications of deep learning methods to medical image processing and segmentation are rapidly becoming the latest technology and resulting in performance improvements in clinical applications. This article provides an overview of the applications, methods, and contents of deep learning approaches for the segmentation of biomedical images.

Keywords

Imaging, Biomedical image, Deep learning, Biomedical image segmentation, Medical image processing

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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