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Deep Learning Based Methods for Biomedical Image Segmentation: A Review

Yıl 2023, Cilt: 12 Sayı: 1, 161 - 187, 10.06.2023
https://doi.org/10.55007/dufed.1181996

Öz

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.

Kaynakça

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

Yıl 2023, Cilt: 12 Sayı: 1, 161 - 187, 10.06.2023
https://doi.org/10.55007/dufed.1181996

Öz

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.

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  • A. BenTaieb and G. Hamarneh, “Topology aware fully convolutional networks for histology gland segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, pp. 460–468, 2016, doi: 10.1007/978-3-319-46723-8_53.
  • H. Qu, G. Riedlinger, P. Wu, Q. Huang, J. Yi, S. De and D. Metaxas, “Joint segmentation and fine-grained classification of nuclei in histopathology images,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, Apr. 08-11, 2019.
  • S. Graham, Q. D. Vu, S. Raza, A. Azam, Y. Tsang, J. Kwak and N. Rajpoot, “Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images,” Medical Image Analysis, vol. 58, 2019, doi: https://doi.org/10.1016/j.media.2019.101563
  • M. Gadermayr, L. Gupta, V. Appel, P. Boor, B. M. Klinkhammer and D. Merhof “Generative adversarial networks for facilitating stain-independent supervised and unsupervised segmentation: a study on kidney histology,” IEEE Transactions on Medical Imaging, vol. 38, no. 10, pp. 2293-2302, 2019, doi: 10.1109/TMI.2019.2899364
  • M. Gadermayr, L. Gupta, B. M. Klinkhammer, P. Boor, and D. Merhof, “Unsupervisedly training GANs for segmenting digital pathology with automatically generated annotations,” arXiv:1805.10059,1 Aug. 2018.
  • A. Kapil, T. Wiestler, S. Lanzmich, A. Silva, K. Steele, M. Rebelatto, G. Schmidt and N. Brieu, “DASGAN--Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images,” arXiv:1906.11118, 26 Jun. 2019.
  • B. Xu, J. Liu, X. Hou, B. Liu, J. Garibaldi, L. O. Ellis, A. Green, L. Shen, G. Qiu, “Look, investigate, and classify: a deep hybrid attention method for breast cancer classification,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, Apr. 8-11, 2019.
  • J. M. Bokhorst, H. Pinckaers, P. Van Zwam, I. Nagtegaal, J. Van der Laak, F. Ciompi, “Learning from sparsely annotated data for semantic segmentation in histopathology images,” Proceedings of Machine Learning Research, vol. 102, pp. 84-91, 2019.
  • W. Bulten, P. Bandi, J. Hoven, R. van de Loo, J. Lotz, N. Weiss, J. Van der Laak, B. Van Ginneken, C. Hulsbergen- van de Kaa and G. Litjens, “Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard,” Sci Rep, vol. 9, no. 864, 2019, doi: 10.1038/s41598-018-37257-4
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Toplam 140 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Derleme
Yazarlar

Tuğba Şentürk 0000-0002-1323-5752

Fatma Latifoğlu 0000-0003-2018-9616

Erken Görünüm Tarihi 6 Haziran 2023
Yayımlanma Tarihi 10 Haziran 2023
Gönderilme Tarihi 29 Eylül 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 1

Kaynak Göster

IEEE T. Şentürk ve F. Latifoğlu, “Biyomedikal Görüntülerin Bölütlenmesine Yönelik Derin Öğrenmeye Dayalı Yöntemler: Bir Gözden Geçirme”, DÜFED, c. 12, sy. 1, ss. 161–187, 2023, doi: 10.55007/dufed.1181996.


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