# Automatic segmentation and volume measurement of anterior visual pathway in brain 3D-T1WI using deep learning

## TL;DR

This study uses a deep learning model called 3D UX-Net to automatically measure the volume of the anterior visual pathway in brain MRI scans, achieving high accuracy compared to manual methods.

## Contribution

The study introduces the 3D UX-Net model as a novel and accurate method for automatic segmentation and volume measurement of the anterior visual pathway in brain T1-weighted imaging.

## Key findings

- 3D UX-Net achieved the highest Dice similarity coefficient (DSC) of 0.893 among tested models.
- The model produced a mean AVP volume of 1446.78 mm³, closely matching manual segmentations.
- Significant sex-based volume differences were found, but no age correlation was observed.

## Abstract

Accurate anterior visual pathway (AVP) segmentation is vital for clinical applications, but manual delineation is time-consuming and resource-intensive. We aim to explore the feasibility of automatic AVP segmentation and volume measurement in brain T1-weighted imaging (T1WI) using the 3D UX-Net deep-learning model.

Clinical data and brain 3D T1WI from 119 adults were retrospectively collected. Two radiologists annotated the AVP course in each participant’s images. The dataset was randomly divided into training (n = 89), validation (n = 15), and test sets (n = 15). A 3D UX-Net segmentation model was trained on the training data, with hyperparameters optimized using the validation set. Model accuracy was evaluated on the test set using Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). The 3D UX-Net’s performance was compared against 3D U-Net, Swin UNEt TRansformers (UNETR), UNETR++, and Swin Soft Mixture Transformer (Swin SMT). The AVP volume in the test set was calculated using the model’s effective voxel volume, with volume difference (VD) assessing measurement accuracy. The average AVP volume across all subjects was derived from 3D UX-Net’s automatic segmentation.

The 3D UX-Net achieved the highest DSC (0.893 ± 0.017), followed by Swin SMT (0.888 ± 0.018), 3D U-Net (0.875 ± 0.019), Swin UNETR (0.870 ± 0.017), and UNETR++ (0.861 ± 0.020). For surface distance metrics, 3D UX-Net demonstrated the lowest median ASSD (0.234 mm [0.188–0.273]). The VD of Swin SMT was significantly lower than that of 3D U-Net (p = 0.008), while no statistically significant differences were observed among other groups. All models exhibited identical HD95 (1 mm [1-1]). Automatic segmentation across all subjects yielded a mean AVP volume of 1446.78 ± 245.62 mm3, closely matching manual segmentations (VD = 0.068 ± 0.064). Significant sex-based volume differences were identified (p < 0.001), but no age correlation was observed.

We provide normative values for the automatic MRI measurement of the AVP in adults. The 3D UX-Net model based on brain T1WI achieves high accuracy in segmenting and measuring the volume of the AVP.