SAM-UNETR: Clinically Significant Prostate Cancer Segmentation Using Transfer Learning From Large Model
Por:
J. ALZATE-GRISALES, A. MORA-RUBIO, F. GARCÍA-GARCÍA, R. TABARES-SOTO and M. DE LA IGLESIA-VAYÁ
Publicada:
1 ene 2023
Resumen:
Prostate cancer (PCa) is one of the leading causes of cancer-related mortality among men worldwide. Accurate and efficient segmentation of clinically significant prostate cancer (csPCa) regions from magnetic resonance imaging (MRI) plays a crucial role in diagnosis, treatment planning, and monitoring of the disease, however, this is a challenging task even for the specialized clinicians. This study presents SAM-UNETR, a novel model for segmenting csPCa regions from MRI images. SAM-UNETR combines a transformer-encoder from the Segment Anything Model (SAM), a versatile segmentation model trained on 11 million images, with a residual-convolution decoder inspired by UNETR. The model uses multiple image modalities and applies prostate zone segmentation, normalization, and data augmentation as preprocessing steps. The performance of SAM-UNETR is compared with three other models using the same strategy and preprocessing. The results show that SAM-UNETR achieves superior reliability and accuracy in csPCa segmentation, especially when using transfer learning for the image encoder. This demonstrates the adaptability of large-scale models for different tasks. SAM-UNETR attains a Dice Score of 0.467 and an AUROC of 0.77 for csPCa prediction.
Filiaciones:
J. ALZATE-GRISALES:
Fdn Fomento Invest Sanitario & Biomed Comunidad Va, Unidad Mixta Imagen Biomed FISABIO CIPF, Valencia 46020, Spain
Univ Autonoma Manizales, Dept Elect & Automat, Manizales 170001, Colombia
A. MORA-RUBIO:
Fdn Fomento Invest Sanitario & Biomed Comunidad Va, Unidad Mixta Imagen Biomed FISABIO CIPF, Valencia 46020, Spain
Univ Autonoma Manizales, Dept Elect & Automat, Manizales 170001, Colombia
:
Principe Felipe Res Ctr CIPF, Bioinformat & Biostat Unit, Valencia 46012, Spain
R. TABARES-SOTO:
Univ Autonoma Manizales, Dept Elect & Automat, Manizales 170001, Colombia
Univ Adolfo Ibanez, Fac Ingn & Ciencias, Santiago 7941169, Chile
Univ Caldas, Dept Sistemas & Informat, Manizales 170001, Colombia
:
Fdn Fomento Invest Sanitario & Biomed Comunidad Va, Unidad Mixta Imagen Biomed FISABIO CIPF, Valencia 46020, Spain
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