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For scleroderma patients, fewer adipose-derived stem cells, because of a history of corticosteroid therapy and a local inflammatory microenvironment, are more important factors, whereas blood supply showed no significant change. Therefore, cell-assisted lipotransfer not only improves the survival rate of transplanted fat but also improves skin texture in bleomycin-induced skin fibrosis nude mice.

We present a new Adaptive Error Correction Net (AEC-Net) to formulate the estimation of Cobb anges from spinal X-rays as a high-precision regression task. Our AEC-Net introduces two novel innovations. (1) The AEC-Net contains two networks calculating landmarks and Cobb angles separately, which robustly solve the disadvantage of ambiguity in X-rays since these networks focus on more features. It effectively handles the nonlinear relationship between input images and quantitative outputs, while explicitly capturing the intrinsic features of input images. (2) Based on the two estimated angles, the AEC-Net proposed a new loss function to calculate the final Cobb angles. The optimization of the loss function is based on a high-precision calculation method. The deep learning structure is used to complete this optimization, which achieves higher accuracy and efficiency. We validate our method with the spinal X-rays dataset of 581 subjects with signs of scoliosis at varying extents. The proposed method achieves high accuracy and robustness on the Cobb angle estimations. Comparing to the exsiting conventional methods suffering from tremendous variability and low reliability caused by high ambiguity and variability around boundaries of the vertebrae, the AEC-Net obtain Cobb angles accurately and robustly, which indicates its great potential in clinical use. The highly accurate Cobb angles produced by our framework can be used by clinicians for comprehensive scoliosis assessment, and possibly be further extended to other clinical applications. INDEX TERMS AEC-Net, Cobb angle estimation, deep learning, direct estimation, high-precision calculation. LIANSHENG WANG, photograph and biography not available at the time of publication.

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