The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration.

Deep-learning-based registration methods can not only save time but also automatically extract deep features from images. In order to obtain better registration performance, many scholars use cascade networks to realize a coarse-to-fine registration progress. However, such cascade networks will increase network parameters by an n-times multiplication factor and entail long training and testing stages. In this paper, we only use a cascade network in the training stage. Unlike others, the role of the second network is to improve the registration performance of the first network and function as an augmented regularization term in the whole process. In the training stage, the mean squared error loss function between the dense deformation field (DDF) with which the second network has been trained and the zero field is added to constrain the learned DDF such that it tends to 0 at each position and to compel the first network to conceive of a better deformation field and improve the network's registration performance. In the testing stage, only the first network is used to estimate a better DDF; the second network is not used again. The advantages of this kind of design are reflected in two aspects: (1) it retains the good registration performance of the cascade network; (2) it retains the time efficiency of the single network in the testing stage. The experimental results show that the proposed method effectively improves the network's registration performance compared to other state-of-the-art methods.

Fragile Watermark in Medical Image Based on Prime Number Distribution Theory.

The research of medical image in the field of anti-counterfeiting and authentication plays a crucial role in the development of hospital digitization. In this paper, a new fragile medical watermarking scheme is proposed using the prime number distribution theorem, chaotic mapping, and Hash. Firstly, an approximate pixel set is constructed according to the distribution of prime numbers, and then the parity of the pixel value is re-granted with chaotic mapping, and Hash. The embedding and extraction of the watermark are done by using logical operations to adjust the pixel value according to the parity of the pixel value and the number of prime numbers contained in the pixel value. The experimental results demonstrate that the proposed scheme is imperceptible, efficient, and safe. Compared with the existing methods, the proposed scheme achieves the binding of medical image and patient information, as well as exhibits excellent detection and positioning capability, and will have good application prospects in medical image content authentication and tampering detection.

(a,k)-Anonymous Scheme for Privacy-Preserving Data Collection in IoT-based Healthcare Services Systems.

The widely use of IoT technologies in healthcare services has pushed forward medical intelligence level of services. However, it also brings potential privacy threat to the data collection. In healthcare services system, health and medical data that contains privacy information are often transmitted among networks, and such privacy information should be protected. Therefore, there is a need for privacy-preserving data collection (PPDC) scheme to protect clients (patients) data. We adopt (a,k)-anonymity model as privacy pretection scheme for data collection, and propose a novel anonymity-based PPDC method for healthcare services in this paper. The threat model is analyzed in the client-server-to-user (CS2U) model. On client-side, we utilize (a,k)-anonymity notion to generate anonymous tuples which can resist possible attack, and adopt a bottom-up clustering method to create clusters that satisfy a base privacy level of (a1,k1)-anonymity. On server-side, we reduce the communication cost through generalization technology, and compress (a1,k1)-anonymous data through an UPGMA-based cluster combination method to make the data meet the deeper level of privacy (a2,k2)-anonymity (a1 ≥ a2, k2 ≥ k1). Theoretical analysis and experimental results prove that our scheme is effective in privacy-preserving and data quality.

BTDA: Two-factor dynamic identity authentication scheme for data trading based on alliance chain.

With the increase in the market share of data trading, the risks such as identity authentication and authority management are increasingly intensified. Aiming at the problems of centralization of identity authentication, dynamic changes of identities, and ambiguity of trading authority in data trading, a two-factor dynamic identity authentication scheme for data trading based on alliance chain (BTDA) is proposed. Firstly, the use of identity certificates is simplified to solve the problems of large calculation and difficult storage. Secondly, a two-factor dynamic authentication strategy is designed, which uses distributed ledger to achieve dynamic identity authentication throughout the data trading. Finally, a simulation experiment is carried out on the proposed scheme. The theoretical comparison and analysis with similar schemes show that the proposed scheme has lower cost, higher authentication efficiency and security, easier authority management, and can be widely used in various fields of data trading scenarios.

Overview of Multi-Modal Brain Tumor MR Image Segmentation.

The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

Resveratrol enhances the radiosensitivity of nasopharyngeal carcinoma cells by downregulating E2F1.

Identification of safe, effective radiosensitizing agents is urgently needed to improve the outcome of radiotherapy in nasopharyngeal cancer (NPC). In this study, we assessed the ability of the polyphenol resveratrol to act as a radiosensitizer in vitro and in vivo. CNE-1 cells were treated with 50 µM resveratrol for 24 h, then irradiated. E2F transcription factor 1 (E2F1) was stably knocked down and overexpressed using lentiviruses. A xenograft model of NPC was established in nude mice using CNE-1 cells. Compared to control DMSO­treated CNE-1 cells, resveratrol inhibited colony-forming ability and induced G1 phase cell cycle arrest. Radiation survival curves confirmed resveratrol significantly sensitized CNE-1 cells, and resveratrol in combination with 2 Gy irradiation synergistically increased apoptosis. Immunoblotting showed resveratrol dose- and time-dependently downregulated E2F1 and phospho-AKT (p-AKT). Knockdown of E2F1 significantly increased radiosensitivity and downregulated p-AKT; overexpression of E2F1 reversed resveratrol-induced radiosensitivity and upregulated p-AKT. In vivo, 50 mg/kg/day resveratrol and 4 Gy irradiation led to significantly lower tumor volume and tumor weight compared to resveratrol or irradiation alone. Our findings show that resveratrol increases the radiosensitivity of NPC cells by downregulating E2F1 and inhibiting p-AKT, and therefore has potential as a radiosensitizer for NPC.

miR-34b inhibits nasopharyngeal carcinoma cell proliferation by targeting ubiquitin-specific peptidase 22.

OBJECTIVES: This study aimed to investigate the precise role of miR-34b in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: The expression of miR-34b and transcription of ubiquitin-specific peptidase 22 (USP22) were examined using quantitative reverse transcription-polymerase chain reaction. Western blot analysis was used to measure the protein expression of USP22. A dualluciferase assay was used to investigate the interaction between miR-34b and USP22. Cell viability was determined by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay. The cell cycle was analyzed by propidium iodide staining followed by flow cytometry analysis. RESULTS: miR-34b was significantly downregulated in NPC tissues and NPC cell lines. Overexpression of miR-34b in NPC SUNE-6-10B cells inhibited cell viability and proliferation. USP22 was highly expressed in NPC cells and promoted cell viability and proliferation. Restoration of USP22 expression could reverse the effect of miR-34b on NPC cell viability and proliferation. CONCLUSION: miR-34b acts as a tumor suppressor in NPC, which is mediated via repression of the oncogene USP22.