Mısır Tohumu Embriyolarının Bölütlenmesinde Tam Evrişimsel Ağ Tabanlı Mimarilerin Tam Bağlı Şartlı Rastgele Alanlar ile Entegrasyonu

Year 2022, , 100 - 111, 28.02.2022

Serdar Alasu , Muhammed Fatih Talu

https://doi.org/10.35414/akufemubid.1013047

Abstract

Haploid ve diploid mısır tohumlarının sınıflandırılması mısır ıslahında önemli bir konudur. R1-nj renk markörü sayesinde haploid ve diploid mısır tohumları embriyolarındaki renklenme farklılıkları dikkate alınarak görsel olarak ayırt edilebilmektedir. Bu nedenle, mısır tohumu embriyolarının bölütlenmesi haploid ve diploid mısır tohumlarının sınıflandırılması için önemli bir ön-işlemdir. Bu çalışmada, mısır tohumu görüntülerinin otomatik embriyo bölütlemesinde tam evrişim ağ tabanlı derin öğrenme mimarilerinin (FCN8s, SegNet ve U-Net) bölütleme performansları değerlendirilmektedir ve bölütleme çıktılarının tam bağlı Şartlı Rastgele Alanlar yöntemiyle düzgünleştirilmesi incelenmektedir. Böylece tam bağlı Şartlı Rastgele Alanların bölütleme sonucuna etkisi araştırılmıştır Ayrıca bu çalışma için mısır tohumu görüntüleri piksel seviyesinde etiketlenerek referans görüntüler elde edilmiş ve haploid ve diploid mısır tohumu görüntüleri için yeni bir semantik görüntü bölütleme veri seti oluşturulmuştur. Çalışma sonuçları göstermiştir ki, tam evrişim ağ tabanlı derin öğrenme mimarileri ile tam bağlı Şartlı Rastgele Alanlar’ın birlikte kullanımı, görüntü bölütleme sonucunu ortalama IoU performans değerlendirme metriğinde FCN8s, SegNet ve U-Net derin öğrenme mimarileri için sırasıyla 0.0139, 0.0076, 0.0024 iyileştirdiği görülmüştür.

Keywords

Mısır Tohumu Embriyo Bölütleme, Semantik Bölütleme, Tam Evrişimsel Ağlar, SegNet, U-Net, Tam Bağlı Şartlı Rastgele Alanlar

Integration of Fully Convolutional Network Based Architectures with Fully Connected Conditional Random Fields in Maize Seed Embryos Segmentation

Abstract

Classification of haploid and diploid maize seeds is an important issue in maize breeding. Thanks to the R1-nj color marker, haploid and diploid maize seeds can be visually distinguished by considering the coloration differences in embryos. Therefore, segmentation of maize seed embryos is an important pre-processing for the classification of haploid and diploid maize seeds. In this study, the segmentation performances of fully convolution network-based deep learning architectures (FCN8s, SegNet and U-Net) in automatic embryo segmentation of maize seed images are evaluated and the smoothing of segmentation outputs with the fully connected Conditional Random Fields method is examined. Thus, the effect of fully connected Conditional Random Fields on the segmentation result was studied. In addition, for this study ground truths were obtained by labeling the maize seed images at the pixel level, and a new semantic image segmentation dataset was created for the haploid and diploid maize seed images. The study results showed that the combined use of full convolution network-based deep learning architectures and fully connected Conditional Random Fields improved the image segmentation result in the mean IoU performance evaluation metric for FCN8s, SegNet and U-Net deep learning architectures by 0.0139, 0.0076, 0.0024, respectively.

Keywords

Maize Seed Embryo Segmentation, Semantic Segmentation, Fully Convolutional Networks, SegNet, U-Net, Fully Connected Conditional Random Fields

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