Light-convolution dense selection u-net (Lds u-net) for ultrasound lateral bony feature segmentation

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

DA-GAN: Learning structured noise removal in ultrasound volume projection imaging for enhanced spine segmentation

Ultrasound volume projection imaging (VPI) has shown to be appealing from a clinical perspective, because of its harmlessness, flexibility, and efficiency in scoliosis assessment. However, the limitations in hardware devices degrade the resultant image content with strong structured noise. Owing to the unavailability of reference data and the unpredictable degradation model, VPI image recovery is a challenging problem. In this paper, we propose a novel framework to learn the structured noise removal from unpaired samples. We introduce the attention mechanism into the generative adversarial network to enhance the learning by focusing on the salient corrupted patterns. We also present a dual adversarial learning strategy and integrate the denoiser with a segmentation model to produce the task-oriented noiseless estimation. Experimental results show that the proposed method can improve both the visual quality and the segmentation accuracy on spine images

Structure-Enhanced Attentive Learning for Spine Segmentation from Ultrasound Volume Projection Images

Automatic spine segmentation, based on ultrasound volume projection imaging (VPI), is of great value in clinical applications to diagnose scoliosis in teenagers. In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. Since the spine bones contain strong prior knowledge of their shapes and positions in ultrasound VPI images, we propose to encode this information into the semantic representations in an attentive manner. We first revisit the self-attention mechanism in representation learning, and then present a strategy to introduce the structural knowledge into the key representation in self-attention. By this means, the network explores both the contextual and structural information in the learned features, and consequently improves the segmentation accuracy. We conduct various experiments to demonstrate that our proposed method achieves promising performance on spine image segmentation, which shows great potential in clinical diagnosis

Composite bioscaffolds incorporating decellularized ECM as a cell-instructive component within hydrogels as in vitro models and cell delivery systems

© Springer Science+Business Media New York 2017. Decellularized tissues represent promising biomaterials, which harness the innate capacity of the tissue-specific extracellular matrix (ECM) to direct cell functions including stem cell proliferation and lineage-specific differentiation. However, bioscaffolds derived exclusively from decellularized ECM offer limited versatility in terms of tuning biomechanical properties, as well as cell–cell and cell–ECM interactions that are important mediators of the cellular response. As an alternative approach, in the current chapter we describe methods for incorporating cryo-milled decellularized tissues as a cell-instructive component within a hydrogel carrier designed to crosslink under mild conditions. This composite strategy can enable in situ cell encapsulation with high cell viability, allowing efficient seeding with a homogeneous distribution of cells and ECM. Detailed protocols are provided for the effective decellularization of human adipose tissue and porcine auricular cartilage, as well as the cryo-milling process used to generate the ECM particles. Further, we describe methods for synthesizing methacrylated chondroitin sulphate (MCS) and for performing UV-initiated and thermally induced crosslinking to form hydrogel carriers for adipose and cartilage regeneration. The hydrogel composites offer great flexibility, and the hydrogel phase, ECM source, particle size, cell type(s) and seeding density can be tuned to promote the desired cellular response. Overall, these systems represent promising platforms for the development of tissue-specific 3-D in vitro cell culture models and in vivo cell delivery systems