Akciğer Bilgisayarlı Tomografi Görüntülerinde Yapay Zekâ Tabanlı Örneğe Duyarlı Semantik Lob Segmentasyonu

Year 2023, , 109 - 115, 31.01.2023

Beyza Sayracı , Mahmut Ağralı , Volkan Kılıç

https://doi.org/10.31590/ejosat.1209632

Cited By: 1

Abstract

Dünya çapında sağlık krizine neden olan Koronavirüs hastalığı (COVID-19) tüm dünyayı etkisi altına almıştır. Ölümcül sonuçlara yol açan akut hipoksemik solunum yetmezliği en çok endişe verici komplikasyondur. COVID-19'un etkisini hafifletmek için tedaviden önce enfekte olan bölge analiz edilmelidir. Bu nedenle göğüs bilgisayarlı tomografi (BT), COVID-19'a ait şiddet düzeyini belirlemek için kullanılan yaygın bir yöntemdir. Ayrıca BT görüntülerinde COVID-19 içeren lob bölgelerinin sayısı radyologların bilateral, multifokal ve multilobar gibi bulguları teşhis etmesine yardımcı olur. Lob bölgeleri radyologlar tarafından manuel olarak ayırt edilir, ancak bu uzun çalışma saatleri nedeniyle yanlış teşhislere neden olur. Bu nedenle lob bölgelerini otomatik olarak çıkarabilen yeni araçlara olan ihtiyaç artmıştır. Evrişim sinir ağları (CNN'ler), BT görüntülerinde lob bölgelerini çıkaran, örneğe duyarlı anlamsal lob segmentasyon görevinde insan hatalarını en aza indirmek için otomatik bir yaklaşım sağlar. Bu makalede, örneğe duyarlı anlamsal lob bölütleme görevinde bir kıyaslama oluşturmak için VGG-16, VGG-19 ve ResNet-50 kullanan DeepLabV3+ gibi CNN tabanlı mimariler kullanıldı. Bölütleme sonuçlarını iyileştirmek için lob bölütlemesinden önce akciğeri çıkarmada görüntülere ön işleme uygulandı. Deneysel değerlendirmeler için akciğer ve lob bölgelerini piksel düzeyinde etiketli 9036 görüntü içeren büyük ölçekli bir veri seti oluşturulmuştur. ResNet-50 kullanan DeepLabV3+, lob bölütlemede sırasıyla zar benzerlik katsayısı (DSC) ve Jaccard benzerlik katsayısı (IOU) açısından en yüksek başarımı sırasıyla ile % 99.59 ve % 99.19 olarak gösterdi. Deneyler, yaklaşımımızın, örneğe duyarlı anlamsal lob bölütleme görevi için birkaç son teknoloji yöntemden daha iyi performans gösterdi. Ayrıca, göğüs BT görüntülerinde lob bölgelerinin bölütlenmesi için LobeChestApp adlı yeni bir masaüstü uygulaması geliştirilmiştir.

Keywords

Yapay Zekâ, Derin Öğrenme, Örneğe Duyarlı Anlamsal Lobe Bölütlemesi, COVID-19, Evrişimsel Sinir Ağları., Artificial Intelligence, Deep Learning, Instance-Aware Semantic Lobe Segmentation, Convolutional Neural Network.

Artificial Intelligence Based Instance-Aware Semantic Lobe Segmentation on Chest Computed Tomography Images

Abstract

The coronavirus disease (COVID-19) has taken the entire world under its influence, causing a worldwide health crisis. The most concerning complication is acute hypoxemic respiratory failure that results in fatal consequences. To alleviate the effect of COVID-19, the infected region should be analyzed before the treatment. Thus, chest computed tomography (CT) is a popular method to determine the severity level of COVID-19. Besides, the number of lobe regions containing COVID-19 on CT images helps radiologists to diagnose the findings, such as bilateral, multifocal, and multilobar. Lobe regions can be distinguished manually by radiologists, but this may result in misdiagnosis due to human intervention. Therefore, in this study, a new tool has been developed that can automatically extract lobe regions using artificial intelligence-based instance-aware semantic lobe segmentation. Convolution neural networks (CNNs) offer automatic feature extraction in the instance-aware semantic lobe segmentation task that extracts the lobe regions on CT images. In this paper, CNN-based architectures, including DeepLabV3+ with VGG-16, VGG-19, and ResNet-50, were utilized to create a benchmark for the instance-aware semantic lobe segmentation task. For further improvement in segmentation results, images were preprocessed to detect the lung region prior to lobe segmentation. In the experimental evaluations, a large-scale dataset including 9036 images with pixel-level annotations for lung and lobe regions, has been created. DeepLabV3+ with ResNet-50 showed the highest performance in terms of dice similarity coefficient (DSC) and intersection over union (IOU) for lobe segmentation at 99.59 % and 99.19 %, respectively. The experiments demonstrated that our approach outperformed several state-of-the-art methods for the instance-aware semantic lobe segmentation task. Furthermore, a new desktop application called LobeChestApp was developed for the segmentation of lobe regions on chest CT images.

Keywords

Artificial Intelligence, Deep Learning, Instance-Aware Semantic Lobe Segmentation, Convolutional Neural Network.

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