Segmentation of Bone Surface from Ultrasound Using a Lightweight Network UBS-Net

OBJECTIVE

Ultrasound-assisted orthopaedic navigation holds promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it is critical to have accurate and real-time bone surface segmentation. Nevertheless, the imaging artifacts and low signal-to-noise ratios in the tomographical B-mode ultrasound (B US) images-create substantial challenges in bone surface detection. In this study, we present an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection. Approach. We present an end-to-end lightweight UBS-Net for bone surface detection, using the U-Net structure as the base framework and a level set loss function for improved sensitivity to bone surface detectability. A dual attention (DA) mechanism is introduced at the end of the encoder, which considers both position and channel information to obtain the correlation between the position and channel dimensions of the feature map, where axial attention (AA) replaces the traditional self-attention (SA) mechanism in the position attention module for better computational efficiency. The position attention and channel attention (CA) are combined with a two-class fusion module for the DA map. The decoding module finally completes the bone surface detection. Main Results. As a result, a frame rate of 21 frames per second (fps) in detection were achieved. It satisfied the real time requirement. The segmentation quality from the proposed approach outperformed the state-of-the-art method with higher accuracy (Dice similarity coefficient: 88.76% vs. 87.22%) in 612 retrospective testing images. Significance. The proposed UBS-Net for bone surface detection in ultrasound achieved outstanding accuracy and real-time performance. The new method out-performed the state-of-the-art methods. It had potential in US-guided orthopaedic surgery applications. .

SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution

Existing approaches to 3D medical image segmentation can be generally categorized into convolution-based or transformer-based methods. While convolutional neural networks (CNNs) demonstrate proficiency in extracting local features, they encounter challenges in capturing global representations. In contrast, the consecutive self-attention modules present in vision transformers excel at capturing long-range dependencies and achieving an expanded receptive field. In this paper, we propose a novel approach, termed SCANeXt, for 3D medical image segmentation. Our method combines the strengths of dual attention (Spatial and Channel Attention) and ConvNeXt to enhance representation learning for 3D medical images. In particular, we propose a novel self-attention mechanism crafted to encompass spatial and channel relationships throughout the entire feature dimension. To further extract multiscale features, we introduce a depth-wise convolution block inspired by ConvNeXt after the dual attention block. Extensive evaluations on three benchmark datasets, namely Synapse, BraTS, and ACDC, demonstrate the effectiveness of our proposed method in terms of accuracy. Our SCANeXt model achieves a state-of-the-art result with a Dice Similarity Score of 95.18% on the ACDC dataset, significantly outperforming current methods.

[Optic cup and disc segmentation model based on linear attention and dual attention]

Glaucoma is one of blind causing diseases. The cup-to-disc ratio is the main basis for glaucoma screening. Therefore, it is of great significance to precisely segment the optic cup and disc. In this article, an optic cup and disc segmentation model based on the linear attention and dual attention is proposed. Firstly, the region of interest is located and cropped according to the characteristics of the optic disc. Secondly, linear attention residual network-34 (ResNet-34) is introduced as a feature extraction network. Finally, channel and spatial dual attention weights are generated by the linear attention output features, which are used to calibrate feature map in the decoder to obtain the optic cup and disc segmentation image. Experimental results show that the intersection over union of the optic disc and cup in Retinal Image Dataset for Optic Nerve Head Segmentation (DRISHTI-GS) dataset are 0.962 3 and 0.856 4, respectively, and the intersection over union of the optic disc and cup in retinal image database for optic nerve evaluation (RIM-ONE-V3) are 0.956 3 and 0.784 4, respectively. The proposed model is better than the comparison algorithm and has certain medical value in the early screening of glaucoma. In addition, this article uses knowledge distillation technology to generate two smaller models, which is beneficial to apply the models to embedded device.