Generalizable deep learning framework for 3D medical image segmentation using limited training data.

ABSTRACT: Medical image segmentation is a critical component in a wide range of clinical applications, enabling the identification and delineation of anatomical structures. This study focuses on segmentation of anatomical structures for 3D printing, virtual surgery planning, and advanced visualization such as virtual or augmented reality. Manual segmentation methods are labor-intensive and can be subjective, leading to inter-observer variability. Machine learning algorithms, particularly deep learning models, have gained traction for automating the process and are now considered state-of-the-art. However, deep-learning methods typically demand large datasets for fine-tuning and powerful graphics cards, limiting their applicability in resource-constrained settings. In this paper we introduce a robust

SUBMITTER: Ekman T 

PROVIDER: S-EPMC11884210 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature