# 3D-NASE: A Novel 3D CT Nasal Attention-Based Segmentation Ensemble

## TL;DR

This paper introduces a new 3D CT segmentation method for nasal structures that improves accuracy and robustness compared to previous techniques.

## Contribution

The novel 3D-NASE framework combines CNN and transformer models with voting strategies for enhanced nasal CT segmentation.

## Key findings

- 3D-NASE achieved a 35.88% improvement in DICE score over previous methods.
- The method reduced the standard deviation by 4.53%, indicating increased robustness.
- The approach combines local and global context for better segmentation accuracy.

## Abstract

Accurate segmentation of the nasal cavity and paranasal sinuses in CT scans is crucial for disease assessment, treatment planning, and surgical navigation. It also facilitates the advanced computational modeling of airflow dynamics and enhances endoscopic surgery preparation. This work presents a novel ensemble framework for 3D nasal CT segmentation that synergistically combines CNN-based and transformer-based architectures, 3D-NASE. By integrating 3D U-Net, UNETR, Swin UNETR, SegResNet, DAF3D, and V-Net with majority and soft voting strategies, our approach leverages both local details and global context to improve segmentation accuracy and robustness. Results on the NasalSeg dataset demonstrate that the proposed ensemble method surpasses previous state-of-the-art results by achieving a 35.88% improvement in the DICE score and reducing the standard deviation by 4.53%. These promising results highlight the potential of our method to advance clinical workflows in diagnosis, treatment planning, and surgical navigation while also promoting further research into computationally efficient and highly accurate segmentation techniques.