Ultrasound spine image segmentation using multi-scale feature fusion Skip-Inception U-Net (SIU-Net)

Banerjee, Sunetra; Liu, Juan; Huang, Zixun; Leung, F.H.F.; Lee, Timothy Tin-Yan; Yang, Deyou; Su, Steven W.; Zheng, Yongping; Ling, Sai Ho

doi:10.1016/j.bbe.2022.02.011

Cited by 18 publications

(4 citation statements)

References 53 publications

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“…Isotope bone scanning (ECT) has high sensitivity but low specificity in the diagnosis of ONFH. A CT scan is helpful for the diagnosis of stage II and III lesions but not helpful for the diagnosis of stage I lesions [ 8 – 10 ].…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

Zhou

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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In order to observe the clinical efficacy of shock waves in the treatment of avascular necrosis of the femoral head in the early and middle stages, a clinical application method of a shock wave in the treatment of avascular necrosis of the femoral head in the early and middle stages was proposed. The method combines the CT image segmentation technology to further segment the hip joint image, thereby speeding up the treatment speed and achieving a better treatment effect. Experimental results show that CT image segmentation takes 10.9 hours with an average time of 8 seconds, which is faster than other methods. The shock wave is an effective treatment method for early avascular necrosis of the femoral head, and this method will become one of the main methods for the clinical treatment of this kind of disease.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

Zhou

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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“…Banerjee et al. ( 2022 ) designed a novel hybrid CNN architecture (SIU-Net) for fully automated bone feature detection, in which dense convolution operations help to further assess the severity of scoliosis. Wang et al.…”

Section: Related Workmentioning

confidence: 99%

PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation

Zhang

Yuan

2023

Evolving Systems

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The lungs of patients with COVID-19 exhibit distinctive lesion features in chest CT images. Fast and accurate segmentation of lesion sites from CT images of patients’ lungs is significant for the diagnosis and monitoring of COVID-19 patients. To this end, we propose a progressive dense residual fusion network named PDRF-Net for COVID-19 lung CT segmentation. Dense skip connections are introduced to capture multi-level contextual information and compensate for the feature loss problem in network delivery. The efficient aggregated residual module is designed for the encoding-decoding structure, which combines a visual transformer and the residual block to enable the network to extract richer and minute-detail features from CT images. Furthermore, we introduce a bilateral channel pixel weighted module to progressively fuse the feature maps obtained from multiple branches. The proposed PDRF-Net obtains good segmentation results on two COVID-19 datasets. Its segmentation performance is superior to baseline by 11.6% and 11.1%, and outperforming other comparative mainstream methods. Thus, PDRF-Net serves as an easy-to-train, high-performance deep learning model that can realize effective segmentation of the COVID-19 lung CT images.

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“…Rahman's [19] work was the first systematic design of exploiting interrelation of improving both bone and shadow segmentation by fusion of CNN and vision transformer to leverage multi-task learning while optimizing accuracy-efficiency trade-off. A novel hybrid CNN architecture, SIU-Net, was proposed by Banerjee to overcome the challenges in US segmentation out of speckle noise and low contrast images [20].…”

Section: Introductionmentioning

confidence: 99%

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

Peng,

Zhang,

Cui

et al. 2024

Biomed. Phys. Eng. Express

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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.

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Ultrasound spine image segmentation using multi-scale feature fusion Skip-Inception U-Net (SIU-Net)

Cited by 18 publications

References 53 publications

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

[Retracted] Clinical Application of Shock Wave in the Treatment of Avascular Necrosis of the Femoral Head in the Early and Middle Stages

PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation

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

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