Bilgisayarlı tomografi taramaları üzerinde maskeli bölgesel-evrişimsel sinir ağları ile karaciğerin otomatik bölütlenmesi

Year 2022, Volume: 37 Issue: 1, 29 - 46, 10.11.2021

Emre Dandıl Mehmet S. Yıldırım Ali Osman Selvi Süleyman Uzun

https://doi.org/10.17341/gazimmfd.774200

Cited By: 1

Abstract

Bilgisayarlı Tomografi (BT) görüntülerinde her bir kesitte ortaya çıkan şekil, sınır ve yoğunluk gibi değişikliklerden dolayı karaciğerin bölütlenmesi zor bir süreç olarak durmaktadır. Diğer bölütleme yöntemleri ile karşılaştırıldığında, derin öğrenme modelleri ile daha başarılı bölütleme sonuçları genel fenomendir. Bu çalışmada, abdomen bölgesinden alınmış BT taramalarındaki kesitler üzerinde karaciğerin bilgisayar destekli otomatik bölütlenmesi için, Maskeli Bölgesel-Evrişimsel Sinir Ağları (Maskeli B-ESA) kullanılarak çoklu-GPU ile hızlandırılmış bir yöntem önerilmiştir. Bu çalışmaya özgü hazırlanan karaciğer BT görüntü veriseti üzerinde, hem tek hem de çift GPU donanımsal yapısı ile deneysel çalışmalar yürütülmüştür. Önerilen yöntem kullanılarak elde edilen sonuçlar ile uzman hekim tarafından bulunan bölütleme sonuçları Dice benzerlik katsayısı (DSC), Jaccard benzerlik katsayısı (JSC), volumetrik örtüşme hatası (VOE), ortalama simetrik yüzey mesafesi (ASD) ve oransal hacim farkı (RVD) ölçüm parametreleri ile karşılaştırılmıştır. Önerilen yaklaşım ile test görüntüleri üzerinde yürütülen deneysel çalışmalarda DSC, JSC, VOE, ASD ve RVD bölütleme başarım metrikleri, sırasıyla 97.32, 94.79, 5.21, 0.390, -1.008 olarak hesaplanmıştır. Bu sonuçlar ile bu çalışma kapsamında önerilen yöntemin, karaciğerin bölütlenmesi için hekimlerin karar verme süreçlerinde yardımcı bir araç olarak kullanılabileceği görülmüştür.

Keywords

Bilgisayarlı Tomografi, Karaciğer Taramaları, Görüntü Bölütleme, Maskeli Bölgesel-Evrişimsel Sinir Ağları, GPU

Supporting Institution

Bilecik Şeyh Edebali Üniversitesi Bilimsel Araştırmalar Koordinatörlüğü

Project Number

2019-01.BŞEÜ.25-02

Thanks

Çalışmanın yazarları olarak, çalışmada kullanılan BT verilerini sağladığı ve kullanımına izin verdiği için T.C. Sağlık Bakanlığı Türkiye Kamu Hastaneleri Kurumu ve Sincan Nafiz Körez Devlet Hastanesi yönetimine teşekkür ederiz.

Automated liver segmentation using Mask R-CNN on computed tomography scans

Abstract

Due to changes such as shape, border and density that occur in the slices of computed tomography (CT) images, liver segmentation remains a difficult process. Compared to other segmentation methods, more successful segmentation results with deep learning models are general phenomenon. In this study, a method accelerated with a multi-GPU is proposed for computer-aided automatic segmentation of the liver on CT scans obtained from the abdominal region using Mask Regional-Convolutional Neural Networks (Mask R-CNN). Experimental studies are conducted on liver CT image datasets to specific for this study with both single and double GPU hardware structure. The results obtained using the proposed method and the segmentation results realized by the specialist physician compared with parameters such as Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), volumetric overlap error (VOE), average symmetric surface distance (ASD) and relative volume difference (RVD) metrics. In experimental studies carried out on the test images with the proposed approach, DSC, JSC, VOE, ASD and RVD segmentation performance metrics are gained as 97.32, 94.79, 5.21, 0.390, -1.008, respectively. With these results, it is seen that the proposed method in this study can be used as a secondary tool in the decision making processes of physicians for the segmentation of the liver.

Keywords

Computed Tomography, Liver Scans, Image Segmentation, Mask R-CNN, GPU

Project Number

2019-01.BŞEÜ.25-02

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