Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

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

doi:10.1109/smc42975.2020.9283335

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…The most-used method is the U-Net. Huang, Z. et al [97] introduced a segmentation method called RSN-U-net, as shown in Figure 8. Aiming at the spot and regular occlusion noise that are prone to appear in ultrasound medical images, this method uses total variance (TV) loss to train the neural network and successfully improves the robustness of spot and regular occlusion noise, effectively segmenting the bone features in ultrasound spine images.…”

Section: Machine Learning Methods For 3d Imagementioning

confidence: 99%

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

Chen

Wang

Yang

et al. 2022

Sensors

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Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.

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Section: Machine Learning Methods For 3d Imagementioning

confidence: 99%

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

Chen

Wang

Yang

et al. 2022

Sensors

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“…Subsequent publications introduce extensions like the 3D U-Net [30], the U-Net++ with redesigned skip connections [31] or the nnU-Net framework for automated configuration of architecture and training process [32]. Most works on bone segmentation in US images rely on the original U-Net [33][34][35][36][37][38][39][40][41][42] as well as the U-Net++ [43]. Several works introduce drop-out during inference [44,45] or training [46] to incorporate segmentation uncertainty.…”

Section: A Us Image Segmentationmentioning

confidence: 99%

Knee Bone Models From Ultrasound

Hohlmann

Brößner²,

Phlippen³

et al. 2023

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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The number of total knee arthroplasties performed world-wide are on a rise. Patient-specific planning and implants may improve surgical outcomes, but require 3D models of the bones involved. Ultrasound may become a cheap and non-harmful imaging modality, if the short comings of segmentation techniques in terms of automation, accuracy and robustness are overcome. Furthermore, any kind of ultrasound-based bone reconstruction must involve some kind of model completion, in order to handle occluded areas, e.g., the frontal femur. A fully-automatic and robust processing pipeline is proposed, generating full bone models from 3D freehand ultrasound scanning. A convolutional neural network is combined with a statistical shape model to segment and extrapolate the bone surface. We evaluate the method in-vivo on 10 subjects, comparing the ultrasound-based model to a magnetic resonance imaging (MRI) reference. The partial freehand 3D record of the femur and tibia bones deviate by 0.7 to 0.8mm from the MRI reference. After completion and on average, the full bone model shows sub-millimetric error in case of the femur and 1.24mm in case of the tibia. Processing of the images is performed in real-time, and the final model fitting step is computed in less than one minute. On average, it took 22 minutes for a full record per subject.

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“…Different related results have been obtained. Huang et al [23] proposed an efficient regularization-based algorithm to address the occlusion issue in VPI images for enhanced spine segmentation. Zhao et al [24] proposed to introduce the structure supervision to the representation learning in a self-attention manner for more effective spine segmentation.…”

Section: B Scoliosis Diagnosis With Ultrasoundmentioning

confidence: 99%

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

Huang

Zhao

Leung

et al. 2022

IEEE Trans. Med. Imaging

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Volume Projection Imaging from ultrasound data is a promising technique to visualize spine features and diagnose Adolescent Idiopathic Scoliosis. In this paper, we present a novel multi-task framework to reduce the scan noise in volume projection images and to segment different spine features simultaneously, which provides an appealing alternative for intelligent scoliosis assessment in clinical applications. Our proposed framework consists of two streams: i) A noise removal stream based on generative adversarial networks, which aims to achieve effective scan noise removal in a weakly-supervised manner, i.e., without paired noisy-clean samples for learning; ii) A spine segmentation stream, which aims to predict accurate bone masks. To establish the interaction between these two tasks, we propose a selective feature-sharing strategy to transfer only the beneficial features, while filtering out the useless or harmful information. We evaluate our proposed framework on both scan noise removal and spine segmentation tasks. The experimental results demonstrate that our proposed method achieves promising performance on both tasks, which provides an appealing approach to facilitating clinical diagnosis.

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Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

Cited by 11 publications

References 17 publications

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

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

Knee Bone Models From Ultrasound

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

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