Automated Skin Lesion Segmentation in Medical Images Using U-Net Architectures

Öz

The biggest challenge, modern methods for laser and surgical treatments face in context of skin diseases is to find the exact boundaries of skin lesions. However, with the integration of deep learning applications with these treatments have shown a great improvement in finding the lesions boundaries. The aim of this study is to comparatively investigate the performances of U-Net and its three improved variations, Residual U-Net, Attention U-Net and Residual Attention U-Net, in skin lesion segmentation. The models were tested on two widely available public datasets, namely ISIC-2016 and ISIC-2017, and the comparison was performed using the same training parameters, image dimensions and evaluation metrics namely accuracy, Dice score and IoU. Attention U-Net model achieved the highest success on ISIC-2016 dataset with 94.4% accuracy, 81.9% Dice score and 81.5% IoU. On the ISIC-2017 dataset, the Residual Attention U-Net model showed superior performance with 92.2% accuracy, 76.9% Dice score and 69.5% IoU. The results show that attention mechanisms and residual structures provide significant contributions to the accurate segmentation of skin lesions and that these architectures have the potential to be used in clinical decision support systems.

Anahtar Kelimeler

• Skin cancer
• lesion segmentation
• deep learning
• U-Net.

U-Net Mimarileri Kullanılarak Tıbbi Görüntülerde Otomatik Cilt Lezyon Segmentasyonu

Öz

Lazer ve cerrahi tedavilerde karşılaşılan en büyük zorluklardan biri, cilt hastalıkları bağlamında cilt lezyonlarının sınırlarını doğru bir şekilde belirlemektir. Ancak, derin öğrenme uygulamalarının bu tedavilerle entegre edilmesi, lezyon sınırlarının tespitinde önemli ilerlemeler sağlamıştır. Bu çalışmanın amacı, U-Net ve onun geliştirilmiş üç varyasyonu olan Residual U-Net, Attention U-Net ve Residual Attention U-Net modellerinin cilt lezyonu segmentasyonundaki performanslarını karşılaştırmalı olarak incelemektir. Modeller, ISIC-2016 ve ISIC-2017 adlı iki yaygın kamuya açık veri kümesi üzerinde test edilmiş ve karşılaştırma; aynı eğitim parametreleri, görüntü boyutları ve doğruluk, Dice skoru ve IoU değerlendirme metrikleri kullanılarak gerçekleştirilmiştir. ISIC-2016 veri kümesinde en yüksek başarıyı %94.4 doğruluk, %81.9 Dice skoru ve %81.5 IoU ile Attention U-Net modeli elde etmiştir. ISIC-2017 veri kümesinde ise Residual Attention U-Net modeli %92.2 doğruluk, %76.9 Dice skoru ve %69.5 IoU ile üstün performans göstermiştir. Sonuçlar, dikkat mekanizmalarının ve artık yapıların cilt lezyonlarının doğru segmentasyonu açısından önemli katkılar sağladığını ve bu mimarilerin klinik karar destek sistemlerinde kullanılabilir potansiyele sahip olduğunu göstermektedir.

Anahtar Kelimeler

• Cilt kanseri
• lezyon segmentasyonu
• derin öğrenme
• U-Net.

Kaynakça

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