Bone surface reconstruction using localized freehand ultrasound imaging

Segmentation of bone surface from ultrasound using a lightweight network UBS-Net

Objective. Ultrasound-assisted orthopaedic navigation held promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it was 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 created substantial challenges in bone surface detection. In this study, we presented an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection.Approach. We presented 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 was introduced at the end of the encoder, which considered both position and channel information to obtain the correlation between the position and channel dimensions of the feature map, where axial attention (AA) replaced the traditional self-attention (SA) mechanism in the position attention module for better computational efficiency. The position attention and channel attention (CA) were combined with a two-class fusion module for the DA map. The decoding module finally completed the bone surface detection.Main Results. As a result, a frame rate of 21 frames per second (fps) in detection were achieved. It outperformed the state-of-the-art method with higher segmentation accuracy (Dice similarity coefficient: 88.76% versus 87.22%) when applied the retrospective ultrasound (US) data from 11 volunteers.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.

Volumetric ultrasound and computer-assisted analysis at the point-of-care: a musculoskeletal exemplar

In this paper we motivate the hypothesis that the use of volumetric ultrasound imaging and automated image analysis tools would improve clinical workflows as well as outcomes at the point-of-care. To make our case, this paper presents results from a rheumatoid arthritis (RA) study where several image analysis techniques have been applied to volumetric ultrasound, highlighting anatomy of interest to better understand disease progression. Pathologies related to RA in joints, manifest themselves commonly as changes in the bone (e.g. erosions) and the region enclosed by the joint-capsule (e.g. synovitis). Automated tools for detecting and segmenting such structures would help significantly towards objective and quantitative assessment of RA in joints. Extracted bone coupled with a simple anatomical model of the joint provides a coarse localization of the joint-capsule region. A probabilistic speckle model is then used to iteratively refine the capsule segmentation. We illustrate the performance of proposed algorithms through quantitative comparisons with expert annotations as well as qualitative results on over 30 scans obtained from 11 subjects.