Abstract

Motivation: Accurate visual pathway (VP) segmentation is critical for clinical diagnosis and surgical planning. Current deep learning-based methods struggle to capture significant context information, impacting the segmentation precision. Goal(s): Improve multi-parametric MRI-based VP segmentation by designing an Adaptive Segmentation Network (ASN). Approach: ASN uses adaptive convolution (AC) to dynamically adjust the kernel based on complementary context, facilitating the integration of contextual information. A spatial attention block selectively extracts relevant regions‘ features in each MRI sequence and fuses them. Results: ASN's effectiveness is validated by segmenting the VP in MR images from two MRI sequences. It surpasses state-of-the-art techniques in VP segmentation. Impact: The introduction of ASN, a new multi-parametric MR images segmentation approach, demonstrates superior performance in visual pathway (VP) segmentation in MR images, surpassing existing state-of-the-art techniques. This novel method effectively incorporates context information, leading to improved segmentation performance.