Postkontrast T1 Agırlıklı Görüntülerde Meningeal Kontrastlanmanın Derin Öğrenme Yöntemi İle Segment

Yıl 2025, Cilt: 47 Sayı: 4, 600 - 605, 18.06.2025

Nevin Aydın , Suzan Şaylısoy , Uğur Toprak , Burcu Mert , Özer Çelik

Öz

Çalışmamızın amacı; postkontrast T1 ağırlıklı görüntülerde meningeal kontrastlanmanın derin öğrenme yöntemi ile segmentasyonunun başarısını değerlendirmektir. Retrospektif olarak 2013-2020 yılları arasında meningeal kontrastlanması olan 83 hastanın postkontrast T1 ağırlıklı sekanslarından elde edilen 313 kesit çalışmaya dahil edildi. Veri seti train – validation- test grubu olarak ayrıldı. Pytorch Unet ile 300 epoch eğitim yapıldı, en iyi model kaydedildi. Birleşim Üzerinde Kesişim (The Intersection over Union, IoU, Jaccard Endeksi) istatistiğinin eşik değeri olarak %50 seçilerek sonuçlar hesaplandı. Toplamda 83 hastanın görüntüleri değerlendirilmiş olup bu hastalardan 36 (%43.4)’sı kadın, 47 (%56.6)’si erkek hasta idi. Hastaların yaş ortalaması ± standart sapması 57.06 ±16.73 idi. 83 hastanın görüntüsünden elde edilen 313 kesitte; 251 kesit eğitime, 31 kesit validasyona, 31 kesitteki etiketler test aşamasına ayrıldı. Test grubunda Doğru Bulunan: 35, Yanlış Bulunan: 12, Bulunamayan: 12 olarak tespit edildi. Çalışmamızda Precision, Sensitivity, F1 Score değerleri sırasıyla %74, %74, %74 olarak hesaplandı. Çalışmamız derin öğrenme temelli U-net mimarisi kullanarak meningeal kontrastlanma alanlarının segmentasyonunda literatürde öncü çalışmalardan biri olup bu alanda yapılacak yeni çalışmalara ihtiyaç vardır.

Anahtar Kelimeler

Derin Öğrenme, Meningeal Kontrastlanma,, Pakimeningeal Kontrastlanma, Leptomeningeal Kontrastlanma, Manyetik Rezonans Görüntüleme

Segmentation of Meningeal Contrast Enhancement in Post-Contrast T1-Weighted Images Using the Deep Learning Method

Öz

To evaluate the success of segmentation of meningeal contrast enhancement on post-contrast T1-weighted images using the deep learning method. The study retrospectively included 313 sections obtained from post-contrast T1-weighted sequences of 83 patients with meningeal enhancement. The dataset was divided into three groups. A total of 300 epochs of training were performed using PyTorch U-Net, and the best model was identified. The results were calculated by selecting 50% as the threshold for the intersection over union statistics. In total, images of 83 patients were evaluated, of whom 36 (43.4%) were female and 47 (56.6%) were male. The mean ± standard deviation of the patients’ age was 57.06 ± 16.73 years. Of the 313 sections obtained, 251 were allocated in the training group, 31 to the validation group, and 31 to the test group. The results of the test group were as follows: 35 true positives, 12 false positives, and 12 false negatives. The precision, sensitivity, and F1 score values were all calculated to be 74%. This is one of the pioneering studies in the literature on the segmentation of meningeal contrast-enhanced areas using the deep learning-based U-net architecture. Further studies are needed in this area

Anahtar Kelimeler

Deep learning method, Meningeal enhancement, Pachymeningeal enhancement, Leptomeningeal enhancement, Magnetic resonance imaging

Kaynakça

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