Transfer learning from T1-weighted to T2-weighted Magnetic resonance sequences for brain image segmentation

Abstract

Magnetic resonance (MR) imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human body. The segmentation of MR images plays a crucial role in medical image analysis, as it enables accurate diagnosis, treatment planning, and monitoring of various diseases and conditions. Due to the lack of sufficient medical images, it is challenging to achieve an accurate segmentation, especially with the application of deep learning networks. The aim of this work is to study transfer learning from T1-weighted (T1-w) to T2-weighted (T2-w) MR sequences to enhance bone segmentation with minimal required computation resources. With the use of an excitation-based convolutional neural networks, four transfer learning mechanisms are proposed: transfer learning without fine tuning, open fine tuning, conservative fine tuning, and hybrid transfer learning. Moreover, a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation technique. The novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and resources. The segmentation results are evaluated using 14 clinical 3D brain MR and CT images. The results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393 ± 0.0007. Although T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation, it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.