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

Coroama, Larisa Gabriela; Diosan, Laura; Telecan, Teodora; Andras, Iulia; Crisan, Nicolae; Andreica, Anca; Caraiani, Cosmin; Lebovici, Andrei; Bálint, Zoltán; Boca, Bianca

Coroama, Larisa Gabriela; Diosan, Laura; Telecan, Teodora; Andras, Iulia; Crisan, Nicolae; Andreica, Anca; Caraiani, Cosmin; Lebovici, Andrei; Bálint, Zoltán; Boca, Bianca.

Afiliação

Coroama LG; Faculty of Mathematics and Computer Science, BabeÈ-Bolyai University, 400084 Cluj-Napoca, Romania.
Diosan L; Faculty of Mathematics and Computer Science, BabeÈ-Bolyai University, 400084 Cluj-Napoca, Romania.
Telecan T; Department of Urology, Faculty of Medicine, Iuliu HaÈieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
Andras I; Department of Urology, Municipal Clinical Hospital, 400139 Cluj-Napoca, Romania.
Crisan N; Department of Urology, Faculty of Medicine, Iuliu HaÈieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
Andreica A; Department of Urology, Municipal Clinical Hospital, 400139 Cluj-Napoca, Romania.
Caraiani C; Department of Urology, Faculty of Medicine, Iuliu HaÈieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
Lebovici A; Department of Urology, Municipal Clinical Hospital, 400139 Cluj-Napoca, Romania.
Bálint Z; Faculty of Mathematics and Computer Science, BabeÈ-Bolyai University, 400084 Cluj-Napoca, Romania.
Boca B; Department of Medical Imaging, Iuliu HaÈieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.

Curr Med Imaging ; 2023 May 22.

Article em En | MEDLINE | ID: mdl-37218191

Palavras-chave

T2 MRI images; UNet architecture; convolutional neuronal network; fully automatic 3D image segmentation; prostate cancer characterization; slim 3D UNet

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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