Brain tumor segmentation with simplified U-Net architecture

Year 2022, Volume: 11 Issue: 4, 856 - 861, 14.10.2022

Özlem Polat

https://doi.org/10.28948/ngumuh.1111082

Abstract

Brain tumors located in the skull are among the health problems that cause serious consequences. Rapid and accurate detection of the brain tumor and segmentation of the tumor region will increase the patient's chance of recovery and survival by ensuring that the patient receives appropriate treatment in the early period. There are many segmentation methods in the literature. The low segmentation accuracy and the very large network structure used are the main disadvantages of the existing methods. In this study, a simplified U-Net deep learning model is used for segmentation on MR images of brain tumors. The model was trained and tested on 3064 MR images from 233 patients, which included the common brain tumors glioma, meningioma, and pituitary. As a result, average 0.86 dice similarity coefficient, 0.76 IoU score, 0.85 sensitivity value and 0.99 pixel accuracy value were obtained. Since the proposed model performs brain tumor segmentation quickly and with high accuracy, it promises to help specialists in the diagnosis of the disease and in determining the appropriate treatment.

Keywords

Brain tumor, Segmentation, Deep learning, U-Net

Sadeleştirilmiş U-Net mimarisi ile beyin tümörü segmentasyonu

Abstract

Kafatası içinde yer alan beyin tümörleri ciddi sonuçlara neden olan sağlık sorunları arasındadır. Beyin tümörünün hızlı ve doğru bir şekilde tespit edilip tümör bölgesinin segmentasyonunun yapılması hastanın erken dönemde uygun tedavi almasını sağlayarak hastanın iyileşme ve hayatta kalma şansını artıracaktır. Literatürde birçok segmentasyon yöntemi bulunmaktadır. Düşük segmentasyon doğruluğu ve kullanılan ağ yapısının çok büyük olması mevcut yöntemlerin ana dezavantajıdır. Bu çalışmada beyin tümörlerinin MR görüntüleri üzerinde segmentasyonu için sadeleştirilmiş U-Net derin öğrenme modeli önerilmektedir. Model, 233 hastadan alınan ve yaygın beyin tümörlerinden gliom, menenjiom ve hipfiz tümörünü içeren 3064 MR görüntüsü üzerinde eğitilip test edilmiştir. Sonuç olarak ortalama 0.86 dice benzerlik katsayısı, 0.76 IoU skoru, 0.85 hassasiyet değeri ve 0.99 piksel doğruluk değeri elde edilmiştir. Önerilen model beyin tümörü segmentasyonunu hızlı ve yüksek doğrulukla gerçekleştirdiğinden hastalığın tanısında ve uygun tedavinin belirlenmesinde uzmanlara yardımcı olması açısından umut vaat etmektedir.

Keywords

Beyin tümörü, Segmentasyon, Derin öğrenme, U-Net

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