Duman A., Powell J., Thomas S., Sun X., Spezi E.
Cardiff University Engineering Research Conference 2023, Cardiff, İngiltere, 5 - 07 Temmuz 2024, ss.3-6, (Tam Metin Bildiri)
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Yayın Türü:
Bildiri / Tam Metin Bildiri
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Doi Numarası:
10.18573/conf1.b
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Basıldığı Şehir:
Cardiff
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Basıldığı Ülke:
İngiltere
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Sayfa Sayıları:
ss.3-6
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Yıldız Teknik Üniversitesi Adresli:
Evet
Özet
Brain tumour segmentation is a hard and time-consuming task to be conducted in the process of radiotherapy planning. Deep Learning (DL) applications have a significant improvement in image segmentation tasks. In this work, we apply DL models such as 2D and 2.5D U-NET to the segmentation task of a brain tumour on the BraTS 2021 dataset and our local dataset. The 2.5D network is a modified version of 2D U-NET by using three slices as an input for each magnetic resonance imaging (MRI) sequence. We achieve the best segmentation results with 2.5D U-NET on BraTS with Dice scores of 86.97%, 91.27% and 94.42% for enhancing tumour, tumour core and whole tumour respectively. On the other hand, our best segmentation result of the GTV delineation on the local dataset is a Dice score of 78.51% for 2D U-NET. Although the result of GTV contours is not improved by 2.5D for the local dataset due to non-fixed voxel size, the Dice scores of ET, TC and WT are improved by the proposed 2.5D U-NET for the BraTS dataset.