SAM-UNETR: Clinically Significant Prostate Cancer Segmentation Using Transfer Learning From Large Model
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 monitorin...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Centro de Investigación Principe Felipe (CIPF) |
| Repositorio: | r-CIPF. Repositorio Institucional Producción Científica del Centro de Investigación Principe Felipe (CIPF) |
| OAI Identifier: | oai:cipf.fundanetsuite.com:p4201 |
| Acceso en línea: | https://cipf.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=4201 |
| Access Level: | acceso abierto |
| Palabra clave: | Image segmentation Training Principal component analysis Transfer learning Task analysis Magnetic resonance imaging Lesions Artificial intelligence deep learning prostate cancer semantic segmentation |
| Sumario: | 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. |
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