Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI.

ABSTRACT:

Purpose

Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset.

Methods

The proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle

SUBMITTER: Zhou W 

PROVIDER: S-EPMC11541278 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature