A Light, 3D UNet-based Architecture for Fully Automatic Segmentation of Prostate Lesions from T2-MRI Images

Prostate magnetic resonance imaging (MRI) has been recently integrated into the pathway of diagnosis of prostate cancer (PCa). However, the lack of an optimal contrast-to-noise ratio hinders automatic recognition of suspicious lesions, thus developing a solution for proper delimitation of the tumour...

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Bibliographische Detailangaben

Veröffentlicht in:	Current medical imaging reviews
Hauptverfasser:	Coroama, Larisa Gabriela, Diosan, Laura, Telecan, Teodora, Andras, Iulia, Crisan, Nicolae, Andreica, Anca, Caraiani, Cosmin, Lebovici, Andrei, Bálint, Zoltán, Boca, Bianca
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United Arab Emirates 01.01.2024
Schlagworte:	fully automatic 3D image segmentation convolutional neuronal network prostate cancer characterization UNet architecture T2 MRI images slim 3D UNet
ISSN:	1573-4056, 1573-4056
Online-Zugang:	Weitere Angaben
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