Light-Convolution Dense Selection U-Net (LDS U-Net) for Ultrasound Lateral Bony Feature Segmentation

Abstract

Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement.

A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

“…When performing the initial screening task for scoliosis, the algorithm was both more accurate and efficient than human experts. Banerjee, S. et al [98] presented a Light-convolution Dense Selection U-Net (LDS U-Net) to identify lateral bony features from ultrasound spine bony features automatically. In order to export the selective features, the method suppresses irrelevant information with a gating mechanism.…”

“…When performing the initial screening task for scoliosis, the algorithm was both more accurate and efficient than human experts. Banerjee, S. et al [98] presented a Light-convolution Dense Selection U-N Net) to identify lateral bony features from ultrasound spine bony features aut In order to export the selective features, the method suppresses irrelevant i with a gating mechanism. In addition, it also uses multi-scale skip-paths to e ture fusion.…”

Knee Bone Models From Ultrasound

“…Other approaches utilize auxiliary hand-crafted features like phase symmetry or bone shadow [47][48][49]. Banerjee et al [50] extended the U-Net with depth wise-separable convolution and residual connections for their LDS-U-Net, similar to [51], who add depth wise-separable convolution next to an attention mechanism to their BoneNet. In order to utilize the characteristic connectivity of bone surfaces in US images, Rahman et al [52] introduce graph convolution to a U-Net like architecture.…”

Joint Spine Segmentation and Noise Removal From Ultrasound Volume Projection Images With Selective Feature Sharing

“…Lyu et al [25] presented a dual-task framework with boundary detection as an auxiliary task to regularize spine segmentation. Banerjee et al [26] proposed a lightweight UNet to perform effective spine segmentation with a low computational burden. In our previous study, we proposed a generative adversarial network with dual adversarial learning (DAGAN) to perform noise removal, which is cascaded with a segmentation network for enhanced spine segmentation [16].…”