Beyin Bilgisayarlı Tomografi Görüntülerinde Derin Öğrenme Tabanlı İskemik İnme Hastalığı Segmentasyonu

Öz

İnme, beyindeki işlevlerin doğru şekilde yerine getirilmesini engelleyen ve kan akışı eksikliği (iskemik) ya da kanama (hemorajik) gibi nedenlerle ortaya çıkan beyin hücre ölümüdür. İskemik inme, kan akışının beyin bölgelerine akmasını önleyen serebrovasküler sistemdeki bir tıkanıklık nedeniyle ortaya çıkan yaygın bir inme türüdür. İnme değerlendirmesinde sıklıkla Bilgisayarlı Tomografi (BT) taraması kullanılmaktadır ve BT görüntüleriyle iskemik inmenin hızlı ve doğru teşhisi, uygun tedavinin belirlenmesi için kritik öneme sahiptir. Uzmanların yoğun programları ve sağlık tesislerine başvuran çok sayıda hastanın olması gibi çeşitli faktörler nedeniyle iskemik inmenin manuel teşhisi hataya açık olabilmektedir. Bu nedenle, bu makalede, BT görüntüleri üzerinden segmentasyon yoluyla iskemik inmeyi otomatik olarak teşhis etmek için derin öğrenme tabanlı bir arayüz geliştirilmiş; bu sayede uzmanların teşhis süresi ve iş yükünün azaltılması hedeflenmiştir. Iskemik inme segmentasyonunda otomatik özellik çıkarımını sağlayan Evrişimli Sinir Ağları (CNN'ler), BT görüntülerindeki hastalıklı bölgeleri işaretlemek için kullanılmıştır. U-Net, U-Net VGG16, U-Net VGG19, Attention U-Net ve ResU-Net gibi CNN tabanlı mimariler, iskemik inme hastalığı segmentasyonunu karşılaştırmak için kullanılmıştır. ResU-Net, segmentasyon performansını daha da artırmak için mimarinin son katmanından sonra bir genişletme evrişim katmanı eklenerek modifiye edilmiştir. Ek olarak, iskemik inme hastalığı bölgesi için gerçek referans değerleri de içeren veri setindeki görüntü sayısını artırmak için veri artırma işlemi gerçekleştirilmiştir. Deneysel sonuçlara dayanarak, genişletme evrişimli olarak modifiye edilmiş ResU-Net, zar benzerlik katsayısı (DSC) ve Jaccard benzerlik katsayısı (IoU) açısından sırasıyla 98,45 % ve 96,95 % ile en yüksek performansı sağlamıştır. Deneysel sonuçlar, modifiye edilmiş ResU-Net mimarisinin iskemik inme hastalığı segmentasyonu için modern yaklaşımlardan daha iyi performans sergilediğini göstermektedir. Ayrıca modifiye edilmiş mimari, iskemik inme bölgesini segmente ederek hastalığın teşhisinde uzmanlara destek sağlayabilen yeni bir masaüstü uygulaması olan BrainSeg'e entegre edilmiştir.

Anahtar Kelimeler

• Yapay Zekâ
• Derin Öğrenme
• İskemik İnme Hastalığı
• Evrişimsel Sinir Ağı

Deep Learning-Based Ischemic Stroke Segmentation on Brain Computed Tomography Images

Abstract

Stroke is brain cell death because of either lack of blood flow (ischemic) or bleeding (hemorrhagic) that prevents the brain from functioning properly in both conditions. Ischemic stroke is a common type of stroke caused by a blockage in the cerebrovascular system that prevents blood from flowing to brain regions and directly blocks blood vessels. Computed tomography (CT) scanning is frequently used in the evaluation of stroke, and rapid and accurate diagnosis of ischemic stroke with CT images is critical for determining the appropriate treatment. The manual diagnosis of ischemic stroke can be error-prone due to several factors, such as the busy schedules of specialists and the large number of patients admitted to healthcare facilities. Therefore, in this paper, a deep learning-based interface was developed to automatically diagnose the ischemic stroke through segmentation on CT images leading to a reduction on the diagnosis time and workload of specialists. Convolutional Neural Networks (CNNs) allow automatic feature extraction in ischemic stroke segmentation, utilized to mark the disease regions from CT images. CNN-based architectures, such as U-Net, U-Net VGG16, U-Net VGG19, Attention U-Net, and ResU-Net, were used to benchmark the ischemic stroke disease segmentation. To further improve the segmentation performance, ResU-Net was modified, adding a dilation convolution layer after the last layer of the architecture. In addition, data augmentation was performed to increase the number of images in the dataset, including the ground truths for the ischemic stroke disease region. Based on the experimental results, our modified ResU-Net with a dilation convolution provides the highest performance for ischemic stroke segmentation in dice similarity coefficient (DSC) and intersection over union (IoU) with 98.45 % and 96.95 %, respectively. The experimental results show that our modified ResU-Net outperforms the state-of-the-art approaches for ischemic stroke disease segmentation. Moreover, the modified architecture has been deployed into a new desktop application called BrainSeg, which can support specialists during the diagnosis of the disease by segmenting ischemic stroke.

Keywords

• Artificial Intelligence
• Deep Learning
• Ischemic Stroke Disease
• Convolutional Neural Network

Kaynakça

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