TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT

TL;DR

TSUBF-Net is a novel 3D segmentation framework that leverages trans-spatial perception and bi-directional feature fusion to improve adenoid hypertrophy segmentation in CT scans, outperforming existing methods.

Contribution

The paper introduces TSUBF-Net with two innovative modules, TSP and BSCF, and a Sobel loss, advancing 3D CT scan segmentation of adenoid hypertrophy.

Findings

Achieves lowest HD95: 7.03, IoU: 85.63, DSC: 92.26 on AHSD dataset.

Demonstrates robustness and effectiveness on multiple public datasets.

Outperforms state-of-the-art segmentation methods.

Abstract

Adenoid hypertrophy stands as a common cause of obstructive sleep apnea-hypopnea syndrome in children. It is characterized by snoring, nasal congestion, and growth disorders. Computed Tomography (CT) emerges as a pivotal medical imaging modality, utilizing X-rays and advanced computational techniques to generate detailed cross-sectional images. Within the realm of pediatric airway assessments, CT imaging provides an insightful perspective on the shape and volume of enlarged adenoids. Despite the advances of deep learning methods for medical imaging analysis, there remains an emptiness in the segmentation of adenoid hypertrophy in CT scans. To address this research gap, we introduce TSUBF-Nett (Trans-Spatial UNet-like Network based on Bi-direction Fusion), a 3D medical image segmentation framework. TSUBF-Net is engineered to effectively discern intricate 3D spatial interlayer features in CT scans and enhance the extraction of boundary-blurring features. Notably, we propose two innovative modules within the U-shaped network architecture:the Trans-Spatial Perception module (TSP) and the Bi-directional Sampling Collaborated Fusion module (BSCF).These two modules are in charge of operating during the sampling process and strategically fusing down-sampled and up-sampled features, respectively. Furthermore, we introduce the Sobel loss term, which optimizes the smoothness of the segmentation results and enhances model accuracy. Extensive 3D segmentation experiments are conducted on several datasets. TSUBF-Net is superior to the state-of-the-art methods with the lowest HD95: 7.03, IoU:85.63, and DSC: 92.26 on our own AHSD dataset. The results in the other two public datasets also demonstrate that our methods can robustly and effectively address the challenges of 3D segmentation in CT scans.