Leveraging General-purpose Models for Enhanced Head and Neck Tumor Segmentation

Entity: Universitat Rovira i Virgili (URV)

Confidenciality: No

Education area(s): Ciència de Dades Biomèdiques

APS: No

Title in different languages: Leveraging General-purpose Models for Enhanced Head and Neck Tumor Segmentation

Abstract: This thesis focuses on improving the segmentation of Head and Neck tumors, a challenging task due to the region's complex anatomy and tumor variability. It evaluates the nnU-Net model, identifying its strengths and limitations. To enhance performance, pre-trained models STU-Net-B and STU-Net-H are introduced, with STU-Net-H significantly improving segmentation accuracy. The thesis proposes a novel framework, nnSAM-3D, which integrates the SAM-Med3D encoder with nnU-Net to leverage multimodal CT and PET data. This approach reaches competitve performance while keeping a low number of model trainable paramters. The work highlights the potential of pre-trained models and suggests future research for better outcomes.

Subject: Imatgeria mèdica

Academic year: 2023-2024

Language: en

Work's public defense date: 2024-09-12

Subject areas: Health sciences

Student: Rodriguez Llana, Sergio

Work's codirector: Diaz Badilla, Emily Natasha

Department: Enginyeria Informàtica i Matemàtiques

Creation date in repository: 2025-03-03

Keywords: Medical imaging, automatic tumor segmentation, deep learning models

Title in original language: Leveraging General-purpose Models for Enhanced Head and Neck Tumor Segmentation

Access Rights: info:eu-repo/semantics/openAccess

Project director: Nagarajan, Bhalaji